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12 March 2021 Research on multi-target detection method based on deep learning
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Proceedings Volume 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications; 117637U (2021) https://doi.org/10.1117/12.2587486
Event: Seventh Symposium on Novel Photoelectronic Detection Technology and Application 2020, 2020, Kunming, China
Abstract
Aiming at the problems of large recognition error and low detection accuracy of small targets in target detection, this paper proposes a multi-target detection algorithm based on deep learning. This method is improved on the basis of YOLOv3. First, the Darknet network is modified, and a set of residual blocks is added at the end of the network to obtain feature maps of 4 scales. Second, replace IOU with DIOU to increase network robustness. Finally, the K-means++ algorithm is used to generate 12 anchor boxes on the training set. The results show that when the input is 576x576, based on the Nvidia 980Ti graphics card, the average accuracy of this algorithm on the VOC 2007 data set reaches 80.1% and the speed reaches 52.64fps, which improves the detection accuracy of multi-target objects while ensuring real-time performance.
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Kang Dai, Xiubao Sui, Liping Wang, Qiuhao Wu, Qian Chen, and Guohua Gu "Research on multi-target detection method based on deep learning", Proc. SPIE 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications, 117637U (12 March 2021); https://doi.org/10.1117/12.2587486
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