Lightweight Detection of Railway Object Intrusion Based on Spectral Pooling and Shuffled-Convolutional Block Attention Module Enhancement
In infrared low-light scenes,railway object intrusion detection faces low detection accuracy,and it is difficult to achieve lightweight real-time detection.Therefore,a lightweight detection method of railway object intrusion based on convolutional block attention module(CBAM)enhancement was proposed.Firstly,the Darknet53 feature extraction network was improved by deep separable convolution to achieve lightweight extraction of railway object intrusion characteristics in infrared low-light scenes.Secondly,semantic-guided infrared spectral pooling was used for feature enhancement to improve the feature quality of infrared image downsampling.Then,a shuffled-CBAM was proposed to achieve feature extraction and fusion of key infrared targets and improve the accuracy of infrared target detection.Finally,the lightweight anchor-free network was used to predict the output result of railway object intrusion,overcoming the deficiency of poor real-time performance due to non-maximum value suppression operation with anchor frame detection,and it reduced calculation load and speeded up the detection efficiency.The experimental results show that the lightweight model has higher detection accuracy,and the size of the model is reduced by 179.01 MB after the improvement.The detection rate is increased to 39 frames/s,which is 3.9 times that of the YOLOv4 method.Compared with other detection methods,the proposed method can detect infrared railway object intrusion quickly and accurately.
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