Abstract
Object detection is a fundamental but challenging issue in the field of generic image analysis; it plays an important role in a wide range of applications and has been receiving special attention in recent years. Although there are enomerous methods exist, an in-depth review of the literature concerning generic detection remains. This paper provides a comprehensive survey of recent advances in visual object detection with deep learning. Covering about 300 publications that we survey 1) region proposal-based object detection methods such as R-CNN, SPPnet, Fast R-CNN, Faster R-CNN, Mask RCN, RFCN, FPN, 2) classification/regression base object detection methods such as YOLO(v2 to v5), SSD, DSSD, RetinaNet, RefineDet, CornerNet, EfficientDet, M2Det 3) Some latest detectors such as, relation network for object detection, DCN v2, NAS FPN. Moreover, five publicly available benchmark datasets and their standard evaluation metrics are also discussed. We mainly focus on the application of deep learning architectures to five major applications, namely Object Detection in Surveillance, Military, Transportation, Medical, and Daily Life. In the survey, we cover a variety of factors affecting the detection performance in detail, such as i) a wide range of object categories and intra-class variations, ii) limited storage capacity and computational power. Finally, we finish the survey by identifying fifteen current trends and promising direction for future research.
Highlights
Object detection is a combination of image classification with precise object localization that provides a complete and proper understanding of the image
YOLOv3 can perform better for detection of a small object due to multiscale predictions compared to medium and more massive sized objects
4) SINGLE SHOT MULTIBOX DETECTOR (SSD) YOLO has difficulty dealing with a generalization of objects in unusual aspect ratio/ configuration, and multiple downsampling operations produce standard features
Summary
Object detection is a combination of image classification with precise object localization that provides a complete and proper understanding of the image. Generic object detection further divided into different categories such as face detection [1], pedestrian detection [2] and skeleton detection [3], etc It is a fundamental computer vision process that provides detailed semantic information of image and video. It has many applications in various fields of life, such as human behavior analysis [4], face recognition [5], image classification [6], medical diagnosis, and autonomous driving [7], [8]. Faster R-CNN generates a region proposal using the additional sub-network [9], while using fixed grid regression in YOLO for object detection tasks [19]. Face and pedestrian images have regular geometric structures; complex variations in structures and layout are common limitations
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