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A Survey on Object Detection Methods in Deep Learning - 2021

A Survey On Object Detection Methods In Deep Learning

Survey Paper on Object Detection Methods In Deep Learning

Research Area:  Machine Learning


Object detection is used almost in all places nowadays. Locating or identifying objects in a video or an image is possible because of computer vision. It is used in picture retrieval, security, observation, computerized vehicle systems machine investigation, etc. There are several techniques which are developed to detect objects. Some of the methods which are used for object detection are also used for face detection. A survey is done on the most popular methods which are used to accomplish these tasks. These methods are CNN, YOLO, SSD, MobileNet, MTCNN, ResNet, RCNN, Viola Jones algorithm and the VGG16. The purpose for which these methods are used is discussed and the results observed by using these methods is also discussed. This survey paper describes and compares the various techniques of mainly object detection and face detection. A conclusion on what method will be suitable for certain object detection or face detection methods can be formed by studying this paper. The survey presented is helpful in identifying the strong points and limitations of the different popular methods used nowadays.

Object Detection
Deep Learning
Deep Learning

Author(s) Name:  Rakhsith L. A; Anusha K. S; Karthik B. E; Arun Nithish D; Kishore Kumar V

Journal name:  

Conferrence name:  2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC)

Publisher name:  IEEE

DOI:  10.1109/ICESC51422.2021.9532809

Volume Information: