A REVIEW ON OBJECT DETECTION SINCE 2005

Recent years have seen a significant increase in interest object detection, which is considered to be one of the most fundamental and demanding computer vision tasks. It might called pinnacle history, because its rapid progress since 2005. If we regard today's identification kind deep learning-powered technical aesthetics, then rewinding clock 2005 would allow us observe wisdom cold war period. This study examines studies on detection context technological advancements that occurred throughout quarter-century. article covers wide range subjects, including historical milestone detectors, metrics, datasets, speed-up strategies, etc. also reviews important traditional detections (DPM HOG), single-stage methods (YOLOR, RetinaNet, SSD, YOLO), two-stage (Mask-RCNN, Faster-RCNN, Fast-RCNN, SPPNet, R-CNN).