A target detection method based on an improved network of stand-alone self-attention mechanisms (YOLOX_SASA) is proposed to address the problems of complex picture backgrounds, slow detection speed, and low detection accuracy. The method firstly improves the speed of target detection by introducing the stand-alone self-attention module in the multi-scale feature fusion part of YOLOX, so that the network can increase the perceptual field while aggregating the neighborhood information. Secondly, by changing the YOLOX binary classification loss function BCE Loss to MultiLabelMargin Loss for label complementation, which in turn improves the target detection accuracy, and by introducing CutMix data enhancement in the training phase to expand the training set and increase the number of samples. Finally, to test the detection effectiveness of the algorithm, simulation experiments are conducted on a homemade small garbage classification dataset and the PASCAL VOC 2007 public dataset. The experimental results show that the method achieves an average accuracy of 93.81% based on satisfying the real-time performance, which is 4.53% better than the original YOLOX algorithm.

Yanyang Zeng, Zihan Zhou, Yang Yu. Study of YOLOX Target Detection Method Based on Stand-Alone Self-Attention. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 12: 29-37. https://doi.org/10.25236/AJCIS.2022.051205.

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