Detection of railway object intrusion under infrared low light based on multi-feature and attention enhancement network
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摘要:
针对红外弱光环境下铁路异物检测时存在目标特征提取不充分、检测精度及实时性低的问题，在CenterNet目标检测模型的基础上，提出了一种红外弱光下多特征融合与注意力增强的无锚框异物检测深度学习模型。在红外目标多尺度特征提取的基础上，引入自适应特征融合(ASFF)模块，充分利用目标高层语义与底层细粒度特征信息，提升红外目标特征提取能力。通过提出的空洞卷积增强注意力模块(Dilated-CBAM)进行关键特征提取，扩大注意力模块感受野范围，克服了原始CenterNet卷积块感受野映射区域变窄、无法检测弱小目标的问题，提升了无锚框网络的检测精度。使用Smooth L1损失函数进行训练，克服了L1损失函数在网络训练过程收敛速度慢及训练不稳定解的问题。通过铁路红外数据集及现场实验测试，结果表明：所提方法较原始CenterNet模型平均检测精度提高了8.03%，检测框置信度提升了31.23%，平均检测速率是Faster R-CNN模型的9.6倍，所提方法在红外弱光环境下能够更加快速准确地检测出铁路异物，主客观评价均优于对比方法。
Abstract: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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图 5 感受野增强注意力Dilated-CBAM模块
Figure 5. Dilated-CBAM model of receptive field enhancement
图 9 弱光环境下铁路异物侵限检测实验结果
Figure 9. Results of railway foreign object intrusion detection experiment in low light environment
图 10 复杂现场铁路异物侵限检测实验结果
Figure 10. Results of railway foreign object intrusion detection experiment in complex environment
表 1 不同方法红外铁路侵限检测指标对比
Table 1. Comparison of infrared railway intrusion detection indicators of different methods
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表 2 本文模型消融实验指标比较
Table 2. Comparison of ablation experimental indexes in the proposed method
基准 ASFF
模块Dilated-CBAM
模块Smooth L1
损失函数mAP/% √ 89.47 √ √ 92.38 √ √ √ 95.66 √ √ √ √ 97.50
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