Progress of object detection: methods and future directions

Object detection, a significant and challenging issue in computer vision with various branches, receives great attention continuously. This paper reviews classic methods of both traditional and deep learning based detectors. Based on overall architecture and anchor, deep learning based detectors can be classified more precisely, including recent improvements. Common datasets and evaluation metrics for accuracy and speed are introduced as well. Besides, future directions, research hotspots and applications are discussed. Novel and captivated tasks such as 3D object detection, camouflaged object detection, rotated object detection are included. Anchor-free detectors are likely to be the mainstream of future object detection methods. Researches are focusing more on sophisticated situations such as small objects, occluded targets and dense distribution.