Nov 13, 2023 · The tunnel crack images collected by optical imaging equipment are used to improve the contrast information of tunnel crack images using the image enhancement algorithm, and this image enhancement algorithm has the function of multi-scale Retinex decomposition with improved central filtering.

In this paper, the design and structure of a tunnel crack detection system are analyzed. On this basis, this paper proposes a new method for crack identification and feature detection using image processing technology.

Feb 7, 2019 · In this work, we present a combinatory deep learning heuristic post-processing scheme in order to identify and classify cracked areas on concrete surfaces for the structural assessment of transportation tunnels.

Jun 1, 2018 · Literature presents different techniques to automatically identify the crack and its depth using image processing techniques. In this research, a detailed survey is conducted to identify the research challenges and the achievements till in this field.

Based on pixel-level processing, an improved crack detection algorithm is proposed using structural analysis for improving the quality of tunnel images. Firstly, image preprocessing transforms the raw images of tunnel surface into binary …

Tunnel engineering is mostly related to the national economy and people's livelihood, and its operation safety is very important. Tunnel safety accidents or pot.

Oct 20, 2023 · To address these issues, a multi-scale Retinex algorithm combined with linear stretching is firstly used to preprocess the images, effectively balancing the lighting. Secondly, an improved adaptive median filter algorithm is used to filter out image noise while effectively preserving the crack edges.

In this study, a system for detecting the presence or absence of cracks and calculating their lengths was developed using image processing technology. The accuracy of the system was examined using crack image data obtained from shear force experiments.

Feb 20, 2020 · In this study, an improved deep fully convolutional neural network, named as CrackSegNet, is proposed to conduct dense pixel-wise crack segmentation. The proposed network consists of a backbone network, dilated convolution, spatial pyramid pooling, and skip connection modules.

Apr 25, 2025 · Contour line feature enhancement is employed to improve the quality and quantity of matching points. Furthermore, a crack detection and quantification framework is proposed, which comprises the YOLOv8-CBAM-seg model for crack detection and an improved orthographic projection (OP) method for crack width quantification.