The Mobile-PSPNet method for real-time segmentation of tunnel lining cracks

In order to solve the problem of applying semantic segmentation networks with high computational complexity for real-time tunnel lining crack detection on mobile platforms with limited computational resources, a real-time segmentation model Mobile-PSPNet based on improved Pyramid Scene Parsing Network was propose to reduce the computing resources of the model. The Resnet50 was replaced with an improved MobileNetV2 lightweight network as the backbone network to reduce model complexity significantly. Besides, the h-swish activation function for mobile devices was introduced in the deep backbone network to compensate for the loss of accuracy due to the replacement of the backbone network. At the same time, the Convolutional Block attention mechanism was introduced in shallow backbone network to enhance the network’ s attention to crack features from both channel and space dimensions, so as to improve the network’ s resistance to interference. Finally, a combined loss function was introduced to calculation the loss globally and locally to deal with sample imbalance problem of crack images. The results show that the crack images can be segmented more accurately by adopting the combined loss function. Mobile-PSPNet takes 26 ms to predict a single 473×473 image on GPU, and the Inter-section over Union (IoU) is 73.74% on the homemade dataset. With comparable accuracy and faster segmentation speed as mainstream models, Mobile-PSPNet is more suitable for real-time detection of tunnel lining cracks on mobile platforms. © 2022, Central South University Press. All rights reserved.