Application of YOLO11 Model with Spatial Pyramid Dilation Convolution (SPD-Conv) and Effective Squeeze-Excitation (EffectiveSE) Fusion in Rail Track Defect Detection

Abstract

With the development of the railway industry and the progression of deep learning technology, object detection algorithms have been gradually applied to track defect detection. To address the issues of low detection efficiency and inadequate accuracy, we developed an improved orbital defect detection algorithm utilizing the YOLO11 model. First, the conventional convolutional layers in the YOLO (You Only Look Once) 11backbone network were substituted with the SPD-Conv (Spatial Pyramid Dilation Convolution) module to enhance the model's detection performance on low-resolution images and small objects. Secondly, the EffectiveSE (Effective Squeeze-Excitation) attention mechanism was integrated into the backbone network to enhance the model's utilization of feature information across various layers, thereby improving its feature representation capability. Finally, a small target detection head was added to the neck network to capture targets of different scales. These improvements help the model identify targets in more difficult tasks and ensure that the neural network allocates more attention to each target instance, thus improving the model's performance and accuracy. In order to verify the effectiveness of this model in track defect detection tasks, we created a track fastener dataset and a track surface dataset and conducted experiments. The mean Average Precision ([email protected]) of the improved algorithm on track fastener dataset and track surface dataset reached 95.9% and 89.5%, respectively, which not only surpasses the original YOLO11 model but also outperforms other widely used object detection algorithms. Our method effectively improves the efficiency and accuracy of track defect detection.

Topics & Concepts