A vision-based nondestructive detection network for rail surface defects.

The inspection and diagnosis of building engineering involve rail surface defect detection, which plays a crucial role in assessing the quality of railway tracks. However, achieving accurate detection remains a significant challenge for infrastructure construction due to the challenging factors, such as complex background, poor texture, the irregular shapes and sizes of rail surface defects, etc. To address these challenges, combined with vision sensor, this paper proposes an automatic vision-based rail surface defect detection network, which leverages a combination of convolutional neural network (CNN) and transformer. Specifically, an effective encoding path based on an improved Res2N-et is presented to obtain stronger contextual information. Meanwhile, to address the limitation of feature representation on global context information, the transformer block is introduced into the bottleneck layer to capture essential global context information. Moreover, an attention-based edge enhancement block is proposed to mitigate the loss of local edge details and strengthen the focus on key feature information, to acquire more discriminant features. To further enrich the feature representation capability, this paper introduces an efficient feature aggregation block, which combines the global features extracted by the transformer and the local features extracted by CNN, to achieve effective feature complementarity and enhances overall detection performance. Combined with public NRSD-MN dataset, proposed model obtains 87.3 % and 77.9 % on PA metric, 85.9 % and 83.4 % in mIoU metric among artificial dataset with natural dataset. Meanwhile, it also obtains 84.0 % PA metric and 87.0 % mIoU metric in SEPR dataset. Experiments have proven the superiority of proposed model though the performance comparison with latest models, which presents an effective and promising rail surface defect detection solution for railway track quality assessment, and can be instrumental in ensuring safe and efficient railway operations. [ABSTRACT FROM AUTHOR]