Multi visual images fusion approach for metro tunnel defects based on saliency optimization of pixel level defect image features

Abstract The multi-vision defect sensing system, lining composed primarily of IRT and RGB cameras, allows for automatic identification and extraction of small surface ailments, greatly enhancing detection efficiency. However, the presence of various issues like train vibration, inconsistent lighting, fluctuations in temperature and humidity leads to the images showing inadequate uniformity in illumination, blurriness, and a decrease in the level of detail. The above issues have led to unsatisfactory fusion processing results for multiple visual images and increased missed detection rates. To address the above-mentioned issue, multi visual images fusion approach for subway tunnel defects based on saliency optimization of pixel level defect image features is proposed. The approach initially analyses the train’s motion status and image blurring conditions. It then eliminates the dynamic blurring in the image. Secondly, Image weights are allocated based on the uniformity of visible light image illumination in the tunnel, as well as real-time temperature and humidity. Finally, image feature extraction and fusion are performed by a U-Net network that integrates channel attention mechanisms. The entire experiment was carried out on a dataset consisting of leakage data from the tunnel lining of Shanghai Metro and tunnel defect data from Beijing Metro. The experimental results demonstrate that this approach improves the image pixel value variation rate by 39.7%, enhances the edge quality by 23%, and outperforms similar approach in terms of average gradient, gradient quality, and sum of difference correlation with improvements of 15.9%, 7.3%, and 26.6% respectively.