Analysis of deep learning object detection methods

Xiaofei Ai; Qiwen He; Pan Zhang

doi:10.1117/12.2675099

25 May 2023 Analysis of deep learning object detection methods

Xiaofei Ai, Qiwen He, Pan Zhang

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 1263613 (2023) https://doi.org/10.1117/12.2675099
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Deep learning-based target detection technology shows excellent performance in healthcare, industry, and transportation. The learning ability of convoluted neural networks (CNN) derives from a combination of feature extraction layers that make the most of a large amount of data. However, they usually require adequate computing and memory resources. Traditional target detection techniques have many limitations, but by using the features of deep learning self-learning, target detection techniques can reduce the complexity of artificial feature extraction. In this paper, the single-stage and two-stage target detection algorithms are analyzed from the angle of detector algorithm, and their respective characteristics are summarized. The most commonly used datasets of target detection are summarized and the evaluation indexes of target detection algorithm are analyzed. Finally, the prospect of the future is given.

Citation Download Citation

Xiaofei Ai, Qiwen He, and Pan Zhang "Analysis of deep learning object detection methods", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 1263613 (25 May 2023); https://doi.org/10.1117/12.2675099

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