A lightweight Mini-YOLOv5s algorithm for small target crack detection in tunnels

Abstract

ABSTRACT The automatic detection and identification of tunnel cracks is very important for safe driving and tunnel maintenance. In order to accelerate the application of automated detection and reduce the deployment cost of target detection algorithm on edge devices, this paper adopts non-destructive testing and proposes a lightweight Mini-YOLOv5s algorithm for small target crack detection in tunnels, which mainly includes DP-C3 module, GDConv module, ECA-C3 module, CARAFE lightweight upsampling operator and loss function IGIoU. Among them, the DP-C3 module uses PConv to reduce the computational complexity of the model, and uses dilated convolution to enrich the receptive field to improve the information loss. The GDConv module improves the feature extraction capability of deep information. The feature fusion of small crack target information is realised by using ECA-C3 module and CARAFE lightweight up-sampling operator. A new loss function IGIoU is used to pay more attention to the contact degree of the boundary frame, so as to minimise the loss and improve the detection accuracy. Experiments show that the detection accuracy of Mini-YOLOv5s on self-made tunnel crack dataset reaches 95%, which compensates for the information loss, while the number of model parameters and GFLOPs are reduced by 35.4% and 30.4% respectively.