Conveyor Belt Deviation Detection for Mineral Mining Applications Based on Attention Mechanism and Boundary Constraints
Year: 2025
Type: article
Abstract: To address the issue of material spillage and equipment wear caused by conveyor belt deviation in complex industrial scenarios, this study proposes a detection method based on an improved U-Net. The approach adopts U-Net as the backbone network, with a ResNet34 encoder to enhance feature extraction capability. At the skip connections, a Multi-scale Adaptive Guidance Attention (MASAG) module is embedded to strengthen the fusion of semantic and detailed features. In the loss function design, a boundary loss is incorporated to improve edge segmentation accuracy. Furthermore, the segmentation results are refined via edge detection and RANSAC regression, and a reference line is constructed based on the physical stability of rollers in the image to enable quantitative measurement of deviation. Experiments on a self-constructed dataset demonstrate that the proposed method achieves higher accuracy (99.77%) compared with the baseline U-Net (99.65%) and also surpasses other categories of approaches, including detection-based (YOLOv5s), anchor-point-based (UFLD), and segmentation-based approaches represented by SEU-Net and DeepLabV3+, thereby exhibiting strong robustness and real-time performance across diverse complex operating conditions. The results validate the effectiveness of this method in practical applications and provide a reliable technical pathway for the development of intelligent monitoring systems for mining conveyor belts.
Source: Sensors
DOI: 10.3390/s25226945
Authors: Long Ma, Jiaming Han, Chong Dong, Ting Fang, Wensheng Liu, Xianhua He
Institutions: Anhui University of Technology, Masteel (China)
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4416226839