An Evaluation of Deep Learning-Based Object Identification

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stage detector architecture
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Published: Dec 9, 2022
DOI: https://doi.org/10.17762/ijritcc.v10i1s.5795
Keywords:
convolution neural networks, machine learning, deep learning, artificial intelligence, multi-layered neural networks.

Main Article Content

Johnson Kolluri
Research Scholar, Department of CSE, National Institute of Technology Mizoram, Aizwal, Mizoram, India.
Ranjita Das
Assistant Professor, Department of CSE, National Institute of Technology Agartala, India.

Abstract

Identification of instances of semantic objects of a particular class, which has been heavily incorporated in people's lives through applications like autonomous driving and security monitoring, is one of the most crucial and challenging areas of computer vision. Recent developments in deep learning networks for detection have improved object detector accuracy. To provide a detailed review of the current state of object detection pipelines, we begin by analyzing the methodologies employed by classical detection models and providing the benchmark datasets used in this study. After that, we'll have a look at the one- and two-stage detectors in detail, before concluding with a summary of several object detection approaches. In addition, we provide a list of both old and new apps. It's not just a single branch of object detection that is examined. Finally, we look at how to utilize various object detection algorithms to create a system that is both efficient and effective. and identify a number of emerging patterns in order to better understand the using the most recent algorithms and doing more study.

Article Details

How to Cite
Kolluri, J. ., & Das, R. . (2022). An Evaluation of Deep Learning-Based Object Identification. International Journal on Recent and Innovation Trends in Computing and Communication, 10(1s), 52–80. https://doi.org/10.17762/ijritcc.v10i1s.5795
Issue
Vol. 10 No. 1s (2022): Special Issue on Enhancement and Innovations in Exploring Engineering
Section
Articles

References

S. K. Pal, A. Pramanik, J. Maiti, and P. Mitra, “Deep learning in multi-object detection and tracking: state of the art,” Appl. Intell., vol. 51, no. 9, pp. 6400–6429, Sep. 2021, doi: 10.1007/s10489-021-02293-7.

S. K. Pal, D. Bhoumik, and D. Bhunia Chakraborty, “Granulated deep learning and Z-numbers in motion detection and object recognition,” Neural Comput. Appl., vol. 32, no. 21, pp. 16533–16548, Nov. 2020, doi: 10.1007/s00521-019-04200-1.

L. Jiao et al., “A Survey of Deep Learning-Based Object Detection,” IEEE Access, vol. 7, pp. 128837–128868, 2019, doi: 10.1109/ACCESS.2019.2939201.

Y. Xiao et al., “A review of object detection based on deep learning,” Multimed. Tools Appl., vol. 79, no. 33–34, pp. 23729–23791, Sep. 2020, doi: 10.1007/s11042-020-08976-6.

C. Hui, B. Xingcan, and L. Mingqi, “Research on Image Edge Detection Method Based on Multi-sensor Data Fusion,” in 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Jun. 2020, pp. 789–792. doi: 10.1109/ICAICA50127.2020.9182548.

I. H. Sarker, “Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions,” SN Comput. Sci., vol. 2, no. 6, p. 420, Nov. 2021, doi: 10.1007/s42979-021-00815-1.

W. Fuhl and E. Kasneci, “Multi Layer Neural Networks as Replacement for Pooling Operations,” Jun. 2020, doi: https://doi.org/10.48550/arXiv.2006.06969.

G. Nguyen et al., “Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey,” Artif. Intell. Rev., vol. 52, no. 1, pp. 77–124, Jun. 2019, doi: 10.1007/s10462-018-09679-z.

B. Playe and V. Stoven, “Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity,” J. Cheminform., vol. 12, no. 1, p. 11, Dec. 2020, doi: 10.1186/s13321-020-0413-0.

M. Naderipour, M. Zarandi, and S. Bastani, “A Multi-layer General Type-2 Fuzzy Community Detection Model in Large-scale Social Networks,” IEEE Trans. Fuzzy Syst., pp. 1–1, 2022, doi: 10.1109/TFUZZ.2022.3153745.

Q. Wang, L. Zhang, Y. Li, and K. Kpalma, “Overview of deep-learning based methods for salient object detection in videos,” Pattern Recognit., vol. 104, p. 107340, Aug. 2020, doi: 10.1016/j.patcog.2020.107340.

G. Huang, “Attention Guided Multi-Scale Regression for Scene Text Detection,” in 2021 2nd International Conference on Computing and Data Science (CDS), Jan. 2021, pp. 498–502. doi: 10.1109/CDS52072.2021.00092.

R. Gupta, D. Saini, and S. Mishra, “Posture detection using Deep Learning for Time Series Data,” in 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Aug. 2020, pp. 740–744. doi: 10.1109/ICSSIT48917.2020.9214223.

I. Hasan, S. Liao, J. Li, S. U. Akram, and L. Shao, “Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond,” Jan. 2022, [Online]. Available: http://arxiv.org/abs/2201.03176

J. Kim and L. Wei, “Performance Analysis of Machine Learning-based Face Detection Algorithms in Face Image Transmission over AWGN and Fading Channels,” in 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), Aug. 2021, pp. 1–5. doi: 10.1109/BMSB53066.2021.9547181.

S. Zhang, Y. Xie, J. Wan, H. Xia, S. Z. Li, and G. Guo, “WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild,” IEEE Trans. Multimed., vol. 22, no. 2, pp. 380–393, Feb. 2020, doi: 10.1109/TMM.2019.2929005.

I. Cvisic, I. Markovic, and I. Petrovic, “Recalibrating the KITTI Dataset Camera Setup for Improved Odometry Accuracy,” in 2021 European Conference on Mobile Robots (ECMR), Aug. 2021, pp. 1–6. doi: 10.1109/ECMR50962.2021.9568821.

J. Kim, J. Bae, G. Park, D. Zhang, and Y. M. Kim, “N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras,” Dec. 2021, [Online]. Available: http://arxiv.org/abs/2112.01041

S. Srivastava, A. V. Divekar, C. Anilkumar, I. Naik, V. Kulkarni, and V. Pattabiraman, “Comparative analysis of deep learning image detection algorithms,” J. Big Data, vol. 8, no. 1, p. 66, Dec. 2021, doi: 10.1186/s40537-021-00434-w.

Y. Kim, J. M. Kim, Z. Akata, and J. Lee, “Large Loss Matters in Weakly Supervised Multi-Label Classification,” Jun. 2022, doi: 10.1103/PhysRevE.105.054214.

I. Krylov, S. Nosov, and V. Sovrasov, “Open Images V5 Text Annotation and Yet Another Mask Text Spotter,” Jun. 2021, [Online]. Available: http://arxiv.org/abs/2106.12326

O. C. Koyun, R. K. Keser, ?. B. Akkaya, and B. U. Töreyin, “Focus-and-Detect: A Small Object Detection Framework for Aerial Images,” Mar. 2022, doi: 10.1016/j.image.2022.116675.

L. Qinghe, C. Weihao, and Q. Hao, “Research on small target detection based on improved Faster R-CNN depth network method,” in 2020 7th International Forum on Electrical Engineering and Automation (IFEEA), Sep. 2020, pp. 833–838. doi: 10.1109/IFEEA51475.2020.00175.

M. Mahendru and S. K. Dubey, “Real Time Object Detection with Audio Feedback using Yolo vs. Yolo_v3,” in 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Jan. 2021, pp. 734–740. doi: 10.1109/Confluence51648.2021.9377064.

Q. Shuai and X. Wu, “Object detection system based on SSD algorithm,” in 2020 International Conference on Culture-oriented Science & Technology (ICCST), Oct. 2020, pp. 141–144. doi: 10.1109/ICCST50977.2020.00033.

X. Long, Z. Zheng, Y. Chi, and R. Liu, “A Mixed Two-stage Object Detector for Image Processing of Power System Applications,” in 2020 IEEE 20th International Conference on Communication Technology (ICCT), Oct. 2020, pp. 1352–1355. doi: 10.1109/ICCT50939.2020.9295843.

A. N. Amudhan, S. R. Vrajesh, A. P. Sudheer, and A. Lijiya, “RFSOD: a lightweight single-stage detector for real-time embedded applications to detect small-size objects,” J. Real-Time Image Process., vol. 19, no. 1, pp. 133–146, Feb. 2022, doi: 10.1007/s11554-021-01170-3.

A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, “A survey of the recent architectures of deep convolutional neural networks,” Artif. Intell. Rev., vol. 53, no. 8, pp. 5455–5516, Dec. 2020, doi: 10.1007/s10462-020-09825-6.

H. Li, X. Yue, Z. Wang, W. Wang, H. Tomiyama, and L. Meng, “A survey of Convolutional Neural Networks —From software to hardware and the applications in measurement,” Meas. Sensors, vol. 18, p. 100080, Dec. 2021, doi: 10.1016/j.measen.2021.100080.

V. Mandal and Y. Adu-Gyamfi, “Object Detection and Tracking Algorithms for Vehicle Counting: A Comparative Analysis,” Jul. 2020, [Online]. Available: http://arxiv.org/abs/2007.16198

A. Benali Amjoud and M. Amrouch, “Convolutional Neural Networks Backbones for Object Detection,” 2020, pp. 282–289. doi: 10.1007/978-3-030-51935-3_30.

X. Zhao, J. Chen, M. Liu, K. Ye, and L. Shen, “Multi-scale Attention-Based Feature Pyramid Networks for Object Detection,” 2021, pp. 405–417. doi: 10.1007/978-3-030-87355-4_34.

Y. Lu, L. Zhang, and W. Xie, “YOLO-compact: An Efficient YOLO Network for Single Category Real-time Object Detection,” in 2020 Chinese Control And Decision Conference (CCDC), Aug. 2020, pp. 1931–1936. doi: 10.1109/CCDC49329.2020.9164580.

C. Zhuang, “DetNAS: Design Object Detection Network via One-Shot Neural Architecture Search,” in 2021 2nd Asia Symposium on Signal Processing (ASSP), Nov. 2021, pp. 28–37. doi: 10.1109/ASSP54407.2021.00013.

A. Krueangsai and S. Supratid, “Effects of Shortcut-Level Amount in Lightweight ResNet of ResNet on Object Recognition with Distinct Number of Categories,” in 2022 International Electrical Engineering Congress (iEECON), Mar. 2022, pp. 1–4. doi: 10.1109/iEECON53204.2022.9741665.

T. Zhou, Y. Zhao, and J. Wu, “ResNeXt and Res2Net Structures for Speaker Verification,” in 2021 IEEE Spoken Language Technology Workshop (SLT), Jan. 2021, pp. 301–307. doi: 10.1109/SLT48900.2021.9383531.

A. Jain, A. Shafi, Q. Anthony, P. Kousha, H. Subramoni, and D. K. Panda, “Hy-Fi: Hybrid Five-Dimensional Parallel DNN Training on High-Performance GPU Clusters,” 2022, pp. 109–130. doi: 10.1007/978-3-031-07312-0_6.

Z. Wentao, G. Lan, and Z. Zhisong, “Garbage Classification and Recognition Based on SqueezeNet,” in 2020 3rd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM), Dec. 2020, pp. 122–125. doi: 10.1109/WCMEIM52463.2020.00032.

B. Koonce, “MobileNet v1,” in Convolutional Neural Networks with Swift for Tensorflow, Berkeley, CA: Apress, 2021, pp. 87–97. doi: 10.1007/978-1-4842-6168-2_8.

S. Ghosh, M. J. Mondal, S. Sen, S. Chatterjee, N. Kar Roy, and S. Patnaik, “A novel approach to detect and classify fruits using ShuffleNet V2,” in 2020 IEEE Applied Signal Processing Conference (ASPCON), Oct. 2020, pp. 163–167. doi: 10.1109/ASPCON49795.2020.9276669.

X. Wu, R. Liu, H. Yang, and Z. Chen, “An Xception Based Convolutional Neural Network for Scene Image Classification with Transfer Learning,” in 2020 2nd International Conference on Information Technology and Computer Application (ITCA), Dec. 2020, pp. 262–267. doi: 10.1109/ITCA52113.2020.00063.

R. J. Wang, X. Li, and C. X. Ling, “Pelee: A Real-Time Object Detection System on Mobile Devices,” Apr. 2018, [Online]. Available: http://arxiv.org/abs/1804.06882

X. Xiao and X. Tian, “Research on Reference Target Detection of Deep Learning Framework Faster-RCNN,” in 2021 5th Annual International Conference on Data Science and Business Analytics (ICDSBA), Sep. 2021, pp. 41–44. doi: 10.1109/ICDSBA53075.2021.00017.

T. D. D and K. V, “Deep Learning based Object Detection using Mask RCNN,” in 2021 6th International Conference on Communication and Electronics Systems (ICCES), Jul. 2021, pp. 1684–1690. doi: 10.1109/ICCES51350.2021.9489152.

M. F. Haque, H.-Y. Lim, and D.-S. Kang, “Object Detection Based on VGG with ResNet Network,” in 2019 International Conference on Electronics, Information, and Communication (ICEIC), Jan. 2019, pp. 1–3. doi: 10.23919/ELINFOCOM.2019.8706476.

R. Ashraf et al., “Deep Convolution Neural Network for Big Data Medical Image Classification,” IEEE Access, vol. 8, pp. 105659–105670, 2020, doi: 10.1109/ACCESS.2020.2998808.

A. M. Hafiz and G. M. Bhat, “A Survey on Instance Segmentation: State of the art,” Jun. 2020, doi: 10.1007/s13735-020-00195-x.

L. Jiang, J. Chen, H. Todo, Z. Tang, S. Liu, and Y. Li, “Application of a Fast RCNN Based on Upper and Lower Layers in Face Recognition,” Comput. Intell. Neurosci., vol. 2021, pp. 1–12, Sep. 2021, doi: 10.1155/2021/9945934.

L. Qiao, Y. Zhao, Z. Li, X. Qiu, J. Wu, and C. Zhang, “DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection,” Aug. 2021, [Online]. Available: http://arxiv.org/abs/2108.09017

S. Li, L. Wang, J. Li, and Y. Yao, “Image Classification Algorithm Based on Improved AlexNet,” J. Phys. Conf. Ser., vol. 1813, no. 1, p. 012051, Feb. 2021, doi: 10.1088/1742-6596/1813/1/012051.

R. Wightman, H. Touvron, and H. Jégou, “ResNet strikes back: An improved training procedure in timm,” Oct. 2021, [Online]. Available: http://arxiv.org/abs/2110.00476

Z. Yu, Y. Dong, J. Cheng, M. Sun, and F. Su, “Research on Face Recognition Classification Based on Improved GoogleNet,” Secur. Commun. Networks, vol. 2022, pp. 1–6, Jan. 2022, doi: 10.1155/2022/7192306.

J. Dai, Y. Li, K. He, and J. Sun, “R-FCN: Object Detection via Region-based Fully Convolutional Networks,” May 2016, [Online]. Available: http://arxiv.org/abs/1605.06409

J. Tang, T. Lu, and J. Wei, “Multi-Scale Approach for Document Detection Based on the Cascade mask RCNN,” in 2021 4th International Conference on Artificial Intelligence and Pattern Recognition, Sep. 2021, pp. 511–517. doi: 10.1145/3488933.3489039.

N. Xiang, C. Pan, and X. Li, “An Object Detection Algorithm Combining FPN Structure With DETR,” in 2021 4th International Conference on Control and Computer Vision, Aug. 2021, pp. 57–63. doi: 10.1145/3484274.3484284.

Z. Qiu, T. Yao, C.-W. Ngo, and T. Mei, “Optimization Planning for 3D ConvNets,” Jan. 2022, [Online]. Available: http://arxiv.org/abs/2201.04021

X. Huang et al., “PP-YOLOv2: A Practical Object Detector,” Apr. 2021, [Online]. Available: http://arxiv.org/abs/2104.10419

M. Alkhaleefah, N. B. Tatini, H.-T. Lee, T.-H. Tan, S.-C. Ma, and Y.-L. Chang, “YOLOv3-mobile for Real-time Pedestrian Detection on Embedded GPU,” in 2021 the 5th International Conference on Graphics and Signal Processing, Jun. 2021, pp. 27–31. doi: 10.1145/3474906.3474915.

R. Araki, T. Hirakawa, T. Yamashita, and H. Fujiyoshi, “MT-DSSD: multi-task deconvolutional single shot detector for object detection, segmentation, and grasping detection,” Adv. Robot., vol. 36, no. 8, pp. 373–387, Apr. 2022, doi: 10.1080/01691864.2022.2043183.

W. Zheng, W. Tang, L. Jiang, and C.-W. Fu, “SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud,” Apr. 2021, [Online]. Available: http://arxiv.org/abs/2104.09804

J. Royo-Miquel, S. Tolu, F. E. T. Schöller, and R. Galeazzi, “RetinaNet Object Detector based on Analog-to-Spiking Neural Network Conversion,” Jun. 2021, [Online]. Available: http://arxiv.org/abs/2106.05624

Q. Zhao et al., “M2Det: A Single-Shot Object Detector Based on Multi-Level Feature Pyramid Network,” Proc. AAAI Conf. Artif. Intell., vol. 33, pp. 9259–9266, Jul. 2019, doi: 10.1609/aaai.v33i01.33019259.

R. Wang et al., “DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems,” Aug. 2020, doi: 10.1145/3442381.3450078.

M. Zhu, G. Hu, S. Li, S. Liu, and S. Wang, “An Effective Ship Detection Method Based on RefineDet in SAR Images,” in 2021 International Conference on Communications, Information System and Computer Engineering (CISCE), May 2021, pp. 377–380. doi: 10.1109/CISCE52179.2021.9445958.

S. Zhang, E. Nezhadarya, H. Fashandi, J. Liu, D. Graham, and M. Shah, “Stochastic Whitening Batch Normalization,” Jun. 2021, [Online]. Available: http://arxiv.org/abs/2106.04413

H. Rezatofighi, N. Tsoi, J. Gwak, A. Sadeghian, I. Reid, and S. Savarese, “Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2019, pp. 658–666. doi: 10.1109/CVPR.2019.00075.

H. Wang, C. Hao, and B. Li, “A water area illegal intrusion detection algorithm based on yolov3 algorithm modification with higher detection accuracy,” in 2021 International Conference on Computer, Blockchain and Financial Development (CBFD), Apr. 2021, pp. 54–59. doi: 10.1109/CBFD52659.2021.00018.

A. Bagaskara and M. Suryanegara, “Evaluation of VGG-16 and VGG-19 Deep Learning Architecture for Classifying Dementia People,” in 2021 4th International Conference of Computer and Informatics Engineering (IC2IE), Sep. 2021, pp. 1–4. doi: 10.1109/IC2IE53219.2021.9649132.

D. R. D. S, B. S. Negara, S. Sanjaya, and E. Satria, “COVID-19 Classification for Chest X-Ray Images using Deep Learning and Resnet-101,” in 2021 International Congress of Advanced Technology and Engineering (ICOTEN), Jul. 2021, pp. 1–4. doi: 10.1109/ICOTEN52080.2021.9493431.

J. Sales, J. Marcato Junior, H. Siqueira, M. De Souza, E. Matsubara, and W. N. Goncalves, “Retinanet Deep Learning-Based Approach to Detect Termite Mounds in Eucalyptus Forests,” in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Jul. 2021, pp. 586–589. doi: 10.1109/IGARSS47720.2021.9555177.

T. Zhang and L. Li, “An Improved Object Detection Algorithm Based on M2Det,” in 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Jun. 2020, pp. 582–585. doi: 10.1109/ICAICA50127.2020.9181938.

S.-H. Lee and H.-C. Chen, “U-SSD: Improved SSD Based on U-Net Architecture for End-to-End Table Detection in Document Images,” Appl. Sci., vol. 11, no. 23, p. 11446, Dec. 2021, doi: 10.3390/app112311446.

C. Lin, Y. Zheng, X. Xiao, and J. Lin, “CXR-RefineDet: Single-Shot Refinement Neural Network for Chest X-Ray Radiograph Based on Multiple Lesions Detection,” J. Healthc. Eng., vol. 2022, pp. 1–11, Jan. 2022, doi: 10.1155/2022/4182191.

J. Park, S. Yoo, J. Park, and H. J. Kim, “Deformable Graph Convolutional Networks,” Dec. 2021, [Online]. Available: http://arxiv.org/abs/2112.14438

L. Aziz, M. S. Bin Haji Salam, U. U. Sheikh, and S. Ayub, “Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review,” IEEE Access, vol. 8, pp. 170461–170495, 2020, doi: 10.1109/ACCESS.2020.3021508.

X. Gu and Y. Fu, “Curvature-Driven Deformable Convolutional Networks for End-To-End Object Detection,” Mob. Inf. Syst., vol. 2022, pp. 1–11, Feb. 2022, doi: 10.1155/2022/7556022.

Y. Wang et al., “Remote sensing image super-resolution and object detection: Benchmark and state of the art,” Expert Syst. Appl., vol. 197, p. 116793, Jul. 2022, doi: 10.1016/j.eswa.2022.116793.

L. Shine and C. V Jiji, “Comparative Analysis of Two Stage and Single Stage Detectors for Anomaly Detection,” in 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Jul. 2021, pp. 1–6. doi: 10.1109/ICCCNT51525.2021.9580079.

Y. Umeki, I. Funahashi, T. Yoshida, and M. Iwahashi, “Salient Object Detection With Importance Degree,” IEEE Access, vol. 8, pp. 147059–147069, 2020, doi: 10.1109/ACCESS.2020.3014886.

G. Singh and A. K. Goel, “Face Detection and Recognition System using Digital Image Processing,” in 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Mar. 2020, pp. 348–352. doi: 10.1109/ICIMIA48430.2020.9074838.

L. Khelifi and M. Mignotte, “Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis,” IEEE Access, vol. 8, pp. 126385–126400, 2020, doi: 10.1109/ACCESS.2020.3008036.

J. Nataprawira, Y. Gu, I. Goncharenko, and S. Kamijo, “Pedestrian Detection on Multispectral Images in Different Lighting Conditions,” in 2021 IEEE International Conference on Consumer Electronics (ICCE), Jan. 2021, pp. 1–5. doi: 10.1109/ICCE50685.2021.9427627.

S. Q. Nisa, A. R. Ismail, M. A. B. M. Ali, and M. S. Khan, “Medical Image Analysis using Deep Learning: A Review,” in 2020 IEEE 7th International Conference on Engineering Technologies and Applied Sciences (ICETAS), Dec. 2020, pp. 1–3. doi: 10.1109/ICETAS51660.2020.9484287.

P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, pp. I-511-I–518. doi: 10.1109/CVPR.2001.990517.

H. Zhou and X. Song, “Lane Detection Algorithm Based on Haar Feature Based Coupled Cascade Classifier,” in 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), Apr. 2021, pp. 286–291. doi: 10.1109/IPEC51340.2021.9421278.

A. Kumar, K. M. Baalamurugan, and B. Balamurugan, “Real-Time Facial Components Detection Using Haar Classifiers,” in 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), May 2022, pp. 01–08. doi: 10.1109/ICAAIC53929.2022.9793034.

Y. Xia, S. Yan, and B. Zhang, “Combination of ACF detector and multi-task CNN for hand detection,” in 2016 IEEE 13th International Conference on Signal Processing (ICSP), Nov. 2016, pp. 601–606. doi: 10.1109/ICSP.2016.7877903.

Y. Li, Y. Tian, J. Tian, and F. Zhou, “An Efficient Method for DPM Code Localization Based on Depthwise Separable Convolution,” IEEE Access, vol. 7, pp. 42014–42023, 2019, doi: 10.1109/ACCESS.2019.2905638.

R. Andrie Asmara, M. Ridwan, and G. Budiprasetyo, “Haar Cascade and Convolutional Neural Network Face Detection in Client-Side for Cloud Computing Face Recognition,” in 2021 International Conference on Electrical and Information Technology (IEIT), Sep. 2021, pp. 1–5. doi: 10.1109/IEIT53149.2021.9587388.

T. Honda and H. Takano, “Development of Feature Extractor for Visible-Light Iris Recognition Using Multi-task CNN,” in 2021 20th International Symposium on Communications and Information Technologies (ISCIT), Oct. 2021, pp. 83–87. doi: 10.1109/ISCIT52804.2021.9590629.

S. Baek, M. Song, J. Jang, G. Kim, and S.-B. Paik, “Face detection in untrained deep neural networks,” Nat. Commun., vol. 12, no. 1, p. 7328, Dec. 2021, doi: 10.1038/s41467-021-27606-9.

L. Hai and H. Guo, “Face Detection with Improved Face R-CNN Training Method,” in 2020 the 3rd International Conference on Control and Computer Vision, Aug. 2020, pp. 22–25. doi: 10.1145/3425577.3425582.

E. S. Babüro?lu, A. Durmu?o?lu, and T. Dereli, “Novel hybrid pair recommendations based on a large-scale comparative study of concept drift detection,” Expert Syst. Appl., vol. 163, p. 113786, Jan. 2021, doi: 10.1016/j.eswa.2020.113786.

A. Z. Agghey, L. J. Mwinuka, S. M. Pandhare, M. A. Dida, and J. D. Ndibwile, “Detection of Username Enumeration Attack on SSH Protocol: Machine Learning Approach,” Symmetry (Basel)., vol. 13, no. 11, p. 2192, Nov. 2021, doi: 10.3390/sym13112192.

Y. Zhong and W. Deng, “OPOM: Customized Invisible Cloak Towards Face Privacy Protection,” IEEE Trans. Pattern Anal. Mach. Intell., pp. 1–1, 2022, doi: 10.1109/TPAMI.2022.3175602.

Z. Yu, J. Yin, Q. Zhang, W. Yang, J.-H. Xue, and Q. Liao, “Hourglass Face Detector for Hard Face,” in 2021 International Joint Conference on Neural Networks (IJCNN), Jul. 2021, pp. 1–8. doi: 10.1109/IJCNN52387.2021.9533674.

Z. Wen, “Large-scale Face Clustering Method Research Based on Deep Learning,” in 2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Dec. 2021, pp. 731–734. doi: 10.1109/MLBDBI54094.2021.00143.

L. Itti and C. Koch, “A saliency-based search mechanism for overt and covert shifts of visual attention,” Vision Res., vol. 40, no. 10–12, pp. 1489–1506, Jun. 2000, doi: 10.1016/S0042-6989(99)00163-7.

W. Wang, Q. Lai, H. Fu, J. Shen, H. Ling, and R. Yang, “Salient Object Detection in the Deep Learning Era: An In-Depth Survey,” Apr. 2019, [Online]. Available: http://arxiv.org/abs/1904.09146

Z. Li and S. Lou, “An Extracting and Labeling Algorithm for Connected Components in Images,” in 2021 International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA), May 2021, pp. 210–213. doi: 10.1109/CAIBDA53561.2021.00051.

S. Valipour, M. Siam, M. Jagersand, and N. Ray, “Recurrent Fully Convolutional Networks for Video Segmentation,” Jun. 2016, [Online]. Available: http://arxiv.org/abs/1606.00487

T. Gomez, T. Fréour, and H. Mouchère, “Metrics for saliency map evaluation of deep learning explanation methods,” Jan. 2022, doi: https://doi.org/10.1101/2021.05.05.21256683.

M. Feng, K. Liu, L. Zhang, H. Yu, Y. Wang, and A. Mian, “Learning from Pixel-Level Noisy Label?: A New Perspective for Light Field Saliency Detection,” Apr. 2022, [Online]. Available: http://arxiv.org/abs/2204.13456

A. Appice, A. Cannarile, A. Falini, D. Malerba, F. Mazzia, and C. Tamborrino, “Leveraging colour-based pseudo-labels to supervise saliency detection in hyperspectral image datasets,” J. Intell. Inf. Syst., vol. 57, no. 3, pp. 423–446, Dec. 2021, doi: 10.1007/s10844-021-00656-7.

Y. Luo, X. Liu, and X. Cao, “Improvement and Comparison of Traditional CNN and SVM Classification Based on Hog Descriptor in Pedestrian Detection,” in 2021 International Conference on Artificial Intelligence and Blockchain Technology (AIBT), Dec. 2021, pp. 12–16. doi: 10.1109/AIBT53261.2021.00009.

W. Li, W. Li, F. Yang, and P. Wang, “Multi-Scale Object Detection in Satellite Imagery Based On YOLT,” in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Jul. 2019, pp. 162–165. doi: 10.1109/IGARSS.2019.8898170.

L. Li, C. Zou, Y. Zheng, Q. Su, H. Fu, and C.-L. Tai, “Sketch-R2CNN?: An RNN-Rasterization-CNN Architecture for Vector Sketch Recognition,” IEEE Trans. Vis. Comput. Graph., vol. 27, no. 9, pp. 3745–3754, Sep. 2021, doi: 10.1109/TVCG.2020.2987626.

L. Bynum, T. Doster, T. H. Emerson, and H. Kvinze, “Rotational Equivariance for Object Classification Using xView,” in IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Sep. 2020, pp. 3684–3687. doi: 10.1109/IGARSS39084.2020.9324015.

G. Cheng, J. Han, P. Zhou, and D. Xu, “Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection,” IEEE Trans. Image Process., vol. 28, no. 1, pp. 265–278, Jan. 2019, doi: 10.1109/TIP.2018.2867198.

S. S. L. Parvathi and H. Jonnadula, “A Comprehensive Survey on Medical Image Blob Detection and Classification Models,” in 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Oct. 2021, pp. 1–6. doi: 10.1109/ICAECA52838.2021.9675575.

Z. Yang, Q. Li, L. Wenyin, and J. Lv, “Shared Multi-view Data Representation for Multi-domain Event Detection,” IEEE Trans. Pattern Anal. Mach. Intell., pp. 1–1, 2019, doi: 10.1109/TPAMI.2019.2893953.

L. Shi, L. Liu, Y. Wu, L. Jiang, and A. Ayorinde, “Event Detection and Multi-source Propagation for Online Social Network Management,” J. Netw. Syst. Manag., vol. 28, no. 1, pp. 1–20, Jan. 2020, doi: 10.1007/s10922-019-09493-0.

K. Xiao, Z. Qian, and B. Qin, “A Survey of Data Representation for Multi-Modality Event Detection and Evolution,” Appl. Sci., vol. 12, no. 4, p. 2204, Feb. 2022, doi: 10.3390/app12042204.

W. Zhao, Y. Hu, H. Wang, X. Wu, and J. Luo, “Boosting Entity-aware Image Captioning with Multi-modal Knowledge Graph,” Jul. 2021, [Online]. Available: http://arxiv.org/abs/2107.11970

F. Hou, Y. Zhang, X. Fu, L. Jiao, and W. Zheng, “The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM,” J. Adv. Transp., vol. 2021, pp. 1–10, Dec. 2021, doi: 10.1155/2021/9513170.

D. Endalie, G. Haile, and W. Taye, “Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia,” Water Supply, vol. 22, no. 3, pp. 3448–3461, Mar. 2022, doi: 10.2166/ws.2021.391.

Y. Zhou, T. Xu, H. Yang, and S. Li, “Improving Spatial Visualization and Mental Rotation Using FORSpatial through Shapes and Letters in Virtual Environment,” IEEE Trans. Learn. Technol., pp. 1–1, 2022, doi: 10.1109/TLT.2022.3170928.

J. Sun, R. Futahashi, and T. Yamanaka, “Improving the Accuracy of Species Identification by Combining Deep Learning With Field Occurrence Records,” Front. Ecol. Evol., vol. 9, Dec. 2021, doi: 10.3389/fevo.2021.762173.

P. Dendorfer et al., “MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking,” Int. J. Comput. Vis., vol. 129, no. 4, pp. 845–881, Apr. 2021, doi: 10.1007/s11263-020-01393-0.

“No Title.” https://motchallenge.net/data/MOT16/

Y. Cao et al., “VisDrone-DET2021: The Vision Meets Drone Object detection Challenge Results,” in 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Oct. 2021, pp. 2847–2854. doi: 10.1109/ICCVW54120.2021.00319.

“No Title.” [Online]. Available: https://pyimagesearch.com/2022/05/02/mean-average-precision-map-using-the-coco-evaluator/.