| Citation: | CHEN Y，WANG Z，LU C T，et al. Detection of railway object intrusion under infrared low light based on multi-feature and attention enhancement network[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（8）：1884-1895 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0591
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There are some problems in railway object intrusion detection in infrared weak light environment, such as insufficient target feature extraction, low detection accuracy and real-time performance. Aiming to those problems, an anchor-free object intrusion depth learning model based on CenterNet target detection model is proposed. This model work with multi-feature fusion and attention enhancement. Firstly, based on the multi-scale feature extraction of infrared targets, the adaptive spatial feature fusion (ASFF) module is used for feature extraction. And to improve the feature extraction ability of infrared targets, this model makes full use of target high-level semantics and low-level fine-grained feature information. Secondly, the key features are extracted through the proposed modified dilated-convolutional block attention module (Dilated-CBAM), which expands the receptive field range of the attention mechanism module. On the one hand, this improvement overcomes the problem that the mapping area of the convolution block receiving field of the original central network becomes narrow and cannot detect weak and small targets; on the other hand, this improvement improves the detection accuracy of the anchor free network. Then, Smooth L1 loss function is used for training, which overcomes the problems of slow convergence speed and unstable solution of L1 loss function in the network training process. Finally, the experimental results are obtained through railway infrared data set and field experiments. The experimental results show that compared with the original CenterNet model, the average detection accuracy of this method is improved by 8.03%, the confidence of the detection frame is improved by 31.23%, and the average detection rate is 9.6 times higher than that of the Faster R-CNN model. This method can detect railway object intrusion more quickly and accurately in the infrared weak light environment, both subjective and objective evaluation are better than the comparison method.
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