Object detection is an important and successful task in the field of computer vision. Object detection is the technique that assists in the recognition, localization, identification, and detection of multiple visual data of objects in an image or video. Object detection uses features to classify the objects and segregate the objects from the other. Object detection using deep learning utilizes the algorithms to detect objects that generate meaningful, accurate, and state-of-the-art results. The benefits of deep learning in object detection are efficient in handling complex models, large data availability, and powerful graphical processing units.
Object detection with deep learning approaches is categorized as one-stage object detectors and two-stage object detectors. One-stage object detectors achieve high inference speed, whereas two-stage object detectors possess high localization and object recognition accuracy. Two-stage detectors algorithm such as Region-based Convolutional Neural networks (RCNN), fast and faster CNN, mask CNN, feature pyramid networks (FPN), spatial pyramid pooling networks (SPPNet), and many more. One stage detectors such as you only look once (YOLO). single-shot detector (SSD), deconvolutional single shot detector (DSSD) RetinaNet, refinement neural network for object detection (RefineDet) and so on.
Some of the latest detectors are relation networks, deformable convolutional networks, to name a few. Application areas of object detection are security, transportation, military, medical, robotics, and retrieval. Some specific applications are pedestrian detection, text and face detection, and remote sensing target detection. Future trends of object detection are the combination of two and one-stage detectors, multi-domain object detection, 3D object detection, advanced medical biometrics, unsupervised and weakly supervised object detection, lightweight detectors, object detection in video, and small object detection.