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This research aims to enhance foreign object detection on underground conveyor belts for improved safety and efficiency.

June 20, 2026Discover Applied SciencesOpen Access

Lightweight YOLO-based foreign object detection for mine conveyor belts

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This research aims to enhance foreign object detection on underground conveyor belts for improved safety and efficiency.
Developed a YOLOv8-based detection framework.
Utilized Contrast Limited Adaptive Histogram Equalization for image preprocessing.
Optimized the network using Alterable Kernel Convolution.
Increased Precision from 86.3% to 88.5%, Recall from 82.4% to 85.2%, and mAP@0.5 from 86.5% to 88.9%.
Reduced parameters from 11.2 M to 9.5 M and FLOPs from 28.4 G to 23.6 G.

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Randomized trial evaluates a YOLOv8-based detection system for mining debris, suggesting improved safety measures.

Zhu et al. (2026) studied this question.

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