Research on intelligent target recognition method based on pattern recognition and deep learning

Guosheng Chen; Wenjun Lian; Fudong Hu; Zuchao Bao; Ruxiang Li; Hang Ling; Jitao Zhong

doi:10.1117/12.2550783

31 January 2020 Research on intelligent target recognition method based on pattern recognition and deep learning

Guosheng Chen, Wenjun Lian, Fudong Hu, Zuchao Bao, Ruxiang Li, Hang Ling, Jitao Zhong

Proceedings Volume 11427, Second Target Recognition and Artificial Intelligence Summit Forum; 114270Y (2020) https://doi.org/10.1117/12.2550783
Event: Second Target Recognition and Artificial Intelligence Summit Forum, 2019, Changchun, China

Abstract

To meet the requirements of automation and intelligence of the vehicle-sighting system, the paper gives an overview of two widely-used target recognition algorithms, including pattern recognition and deep learning. Four typical algorithms, HOG-cascading-Adaboost algorithm and surf combining SVM algorithm, which belong to pattern recognition, CNN network and YOLOv3 network, which belong to deep learning, are elaborated in detail. Different algorithms are used to identify images in the same test set in the experiment, and the performance of each algorithm is compared from three aspects, recognition rate, recall rate and recognition time. Finally, it can be concluded that YOLOv3 algorithm is better for target recognition when concerning the recognition rate and recall rate, with the recognition rate as high as 95.8% and fewer targets missed. Considering the real-time effect, the pattern recognition algorithm has less recognition time but the recognition rate reduces. Therefore, the recognition time and recognition rate should have a compromise in practical application.

Citation Download Citation

Guosheng Chen, Wenjun Lian, Fudong Hu, Zuchao Bao, Ruxiang Li, Hang Ling, and Jitao Zhong "Research on intelligent target recognition method based on pattern recognition and deep learning", Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114270Y (31 January 2020); https://doi.org/10.1117/12.2550783

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