SCBAM-DeepLab: foreign object intrusion detection method for the railway catenary

The intrusion of foreign objects into the railway catenary seriously affects the safety of railway operation. In response to the low accuracy and poor real-time performance of current image processing algorithms in detecting foreign objects in the catenary, this paper proposes a lightweight semantic segmentation model SCBAM-DeepLab. This model is not limited to a specific type of foreign objects. Based on the semantic segmentation results of catenary's components, it detects the geometric relationship between invading foreign objects and catenary's components, which meets the operational environment requirements of the real catenary. Based on the lightweight network MobileNetV3, SCBAMDeepLab further simplifies the model parameters and improves the speed of foreign object detection according to the requirements of foreign object detection. Secondly, a module called Superior Convolutional Block Attention Module (SCBAM) was designed to enhance semantic information and the relationships between feature layers, improving the segmentation progress of the components. The model innovatively established a foreign object detection algorithm based on geometric features, analyzing the geometric features of each component to determine whether there is foreign object intrusion. The experimental results show that compared to other advanced semantic segmentation models, the mean intersection over union (MIoU) of this model has increased by about 4%, and the latency has decreased by about 60%. In addition, the mean average precision (mAP) of foreign object detection based on geometric features reaches 86%. SCBAM-DeepLab has improved the detection speed and accuracy of foreign object intrusion with unknown types.