Object Detection Performance: A Comparative Study

Abstract Object detection is a critical task in computer vision with applications in many domains. Recent advances in deep learning have led to significant improvements in the performance of object detectors. This paper presents a comparative performance analysis of generic object detectors, with a focus on single-stage and two-stage detectors. The paper first discusses the taxonomy of object detection algorithms, and then presents a detailed performance comparison of single-stage and two-stage detectors. The performance of different detectors was evaluated on two different datasets, Microsoft COCO and PASCAL VOC 2012. The results showed that DetectoRS is a state-of-the-art two-stage object detector, outperforms all other two-stage models. While YOLOv4 and FCOS are the two most accurate single-stage detectors. The comparative results also show that single-stage detectors are generally less accurate than two-stage detectors, but they are typically faster. The paper also includes the strengths and weaknesses of different object detection approaches and identifies promising directions for future research.