Comparative Analysis of Advanced Deep Learning Algorithms for Object Detection

Object detection is a crucial task in computer vision that involves identifying and locating objects within images. Numerous deep learning methods, each with unique advantages and disadvantages, have been put out to address this challenge. This paper provides a comprehensive comparison of four prominent object detection models: YOLOv5, FCOS, DETR, and RetinaNet. The evaluation is based on standard metrics such as mean average precision (mAP) and mean average recall (mAR), as well as the speed and efficiency of the models. The results show that DETR achieves the highest accuracy across different intersection over union (IoU) thresholds and object sizes, while YOLOv5 excels in speed and real-time processing. FCOS and RetinaNet offer moderate performance on both fronts but may benefit from further optimization and fine-tuning. The implementation specifics, architectural elements, and mathematical formulations of each model are also covered in the study, offering insights that may direct future object detection research and applications.