Summary of object detection based on convolutional neural network

Xuejiao Wang; Min Zhi

doi:10.1117/12.2557219

3 January 2020 Summary of object detection based on convolutional neural network

Xuejiao Wang, Min Zhi

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113730L (2020) https://doi.org/10.1117/12.2557219
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

Object detection is one of the most basic and central task in computer vision. Its task is to find all the interested objects in the image, and determine the category and location of the objects. Object detection is widely used and has strong practical value and research prospects. Applications include face detection, pedestrian detection and vehicle detection. In recent years, with the development of convolutional neural network, significant breakthroughs have been made in object detection. This paper describes in detail the classification of object detection algorithms based on deep learning. The algorithms are mainly divided into one-stage object algorithm and two-stage object algorithm, and the general data sets and performance indicators of object detection.

Citation Download Citation

Xuejiao Wang and Min Zhi "Summary of object detection based on convolutional neural network", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113730L (3 January 2020); https://doi.org/10.1117/12.2557219

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