ArticleScientific reports2024
Semantic segmentation of thermal defects in belt conveyor idlers using thermal image augmentation and U-Net-based convolutional neural networks.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Detection and Analysis of Conveyor Belt Damage: A Review of Sensing Technologies and Signal-Based Approaches.Sensors (Basel, Switzerland) · 2026Review
- Heterogeneous Information Fusion for Robot-Based Automated Monitoring of Bearings in Harsh Environments via Ensemble of Classifiers with Dynamic Weighted Voting.Sensors (Basel, Switzerland) · 2025Article
- Advanced Image Analytics for Mobile Robot-Based Condition Monitoring in Hazardous Environments: A Comprehensive Thermal Defect Processing Framework.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
The belt conveyor (BC) is the main means of horizontal transportation of bulk materials at mining sites. The sudden fault in BC modules may cause unexpected stops in production lines. With the increasing number of applications of inspection mobile robots in condition monitoring (CM) of industrial infrastructure in hazardous environments, in this article we introduce an image processing pipeline for automatic segmentation of thermal defects in thermal images captured from BC idlers using a mobile robot. This study follows the fact that CM of idler temperature is an important task for preventing sudden breakdowns in BC system networks. We compared the performance of three different types of U-Net-based convolutional neural network architectures for the identification of thermal anomalies using a small number of hand-labeled thermal images. Experiments on the test data set showed that the attention residual U-Net with binary cross entropy as the loss function handled the semantic segmentation problem better than our previous research and other studied U-Net variations.
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