Title: Deep Learning-Based Object Detection: An Investigation
Abstract: Computer vision has one most important and challenging problem of object detection because of its wide application in field of deep learning such as medical image analysis and security monitoring autonomous driving. Object detection tasks have been greatly improved as object detection has compact association with video evaluation and image processing, and it has enticed the notice of researchers in adjunct years and describe the reference datasets at the beginning. This paper provides a complete review of a range of object detection technique, in a structured way detailing about the two-stage and one-stage detector, including the algorithms used both in detectors and in R-CNN, fast R- CNN and faster R-CNN. R-CNN, YOLO, SSD mask, etc. Also, we list the traditional (only detect object and its type) and new app (object detection with analysis and learning). Few indicative divisions of object detection are also discussed, and eventually, the performance of all models used in a one-stage and two-stage detector is discussed. We too in short examine various distinct jobs, together with projecting, face detection, object detection and pedestrian detection. Finally, various budding orientation and trends are furnished that assist as challenges or recommendation for upcoming prospective job.
Publication Year: 2022
Publication Date: 2022-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
Access and Citation
Cited By Count: 7
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