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Vehicle detection plays a crucial role in traffic monitoring systems, but it often faces challenges like occlusion, vehicle size, and shape variations. Existing systems struggle with misinterpreting parts of other vehicles as actual vehicles, leading to losses and distorted shapes. To address these issues, a new approach called ElegantYOLO is introduced in this study. ElegantYOLO combines elements from LittleYOLO-SPP and ShortYOLO-CSP while modifying baseline network layer depths using the improved Ghost Module Extended connection method to reduce computational costs. The model's learning capabilities are improved by incorporating spatial attributes through the concatenation of Spatial Pooling blocks. The study employs the Alpha-IoU as the bounding box loss function, minimizing the disparity between predicted and ground truth boxes. This enhances vehicle detection accuracy and robustness. Additionally, the study uses the slicing-aided hyper inference (SAHI) technique, which allows the lightweight backbone network to capture more detailed vehicle information by processing higher-resolution images. Through extensive testing on various datasets such as PASCAL VOC 2007, 2012, and MS COCO 2014, the proposed model not only excels in detecting small vehicles but also demonstrates improved detection accuracy across different environmental conditions. The performance of ElegantYOLO surpasses both LittleYOLO and ShortYOLO by achieving an almost 10% higher mean average precision (mAP). Specifically, the model achieves outstanding results on PASCAL VOC and COCO datasets, with mAPs of 96.45% and 79.28%, respectively. Moreover, the proposed technique significantly enhances accuracy while reducing detection time.