Development of a deep learning-based foreign object detection algorithm for coal mine conveyor belts

In view of the poor recognition performance of the existing foreign object detection models for coal mine conveyor belts in the complex underground environment, they are prone to false detection and missed detection of slender foreign objects and small target foreign objects. Moreover, the models ar...

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Vydáno v:Scientific reports Ročník 15; číslo 1; s. 42291 - 25
Hlavní autoři: Ling, Jierui, Fu, Zhibo, Yuan, Xinpeng, Ling, Ruining
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 27.11.2025
Nature Publishing Group
Nature Portfolio
Témata:
639/166
639/705/117
639/705/258
639/705/794
Accuracy
Algorithms
Coal
Coal industry
Coal mines
Coal mining
Computer applications
Computer vision
Deep learning
Edge equipment
Efficiency
Humanities and Social Sciences
Image processing
Image segmentation
Lightweight
Localization
Mines
multidisciplinary
Real-time detection
Science
Science (multidisciplinary)
Shared convolutional layers
YOLOv11
Image segmentation
Shared convolutional layers
YOLOv11
Real-time detection
Edge equipment
Lightweight
ISSN:2045-2322, 2045-2322
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Shrnutí:In view of the poor recognition performance of the existing foreign object detection models for coal mine conveyor belts in the complex underground environment, they are prone to false detection and missed detection of slender foreign objects and small target foreign objects. Moreover, the models are large in size, difficult to deploy on edge devices, and the detection methods are slow, have numerous parameters, and involve a large amount of computation. A foreign object detection algorithm for downhole conveyor belts based on the improved YOLOv11 is proposed. Firstly, the ADown downsampling module is incorporated to enhance the detection performance for small defects and reduce the number of parameters. Secondly, the SegNext attention mechanism is integrated to enhance the model’s performance in image segmentation. Thirdly, the C3k2 module is optimized by integrating the Light-weight Context Guided mechanism from the CGNet framework. This enhancement significantly boosts the model’s deployment flexibility and detection speed in complex underground environments. Finally, the lightweight detection head, LSCD, is utilized to enhance the model’s capability in handling multi-scale features. This is achieved through the implementation of shared convolutional layers, which effectively reduce the computational load and parameter size. Moreover, the effectiveness of the enhanced model is further validated through extensive experimental comparisons. The experimental results show that, compared with the original model, the improved model has a 1.5% increase in mAP, a 1.2% increase in Precision, a 2% increase in Recall, a 28% reduction in the number of parameters, a 33% decrease in computational load, and a 29% reduction in the model storage size. This indicates the effectiveness of this method in the detection of foreign objects in coal mine conveyor belts. It has important reference significance for the real-time detection of foreign objects in the conveyor belts of underground coal mines.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-22636-5