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Enhanced foreign body detection on coal mine conveyor belts using improved DLEA and lightweight SARC-DETR model

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Abstract

Conveyor belts in coal mines are critical for coal extraction and safety. Detecting foreign objects in low-light underground environments is challenging. This paper presents an enhanced foreign body detection algorithm using an improved Dual-Model Low-Light Enhancement Algorithm (DLEA) and a lightweight Star Attention Region-based Convolutional Detection Transformer (SARC-DETR). The DLEA improves image quality in low-light conditions, while SARC-DETR, with its StarNet backbone and Efficient Additive Attention mechanism, reduces computational costs without compromising accuracy. A lightweight dynamic group efficient module network is proposed for optimized feature extraction, and the CIoU loss function further enhances positioning accuracy. Experimental results demonstrate a 4.7% precision improvement, a 2.7% increase in average precision, a 47.01% reduction in parameters, and an inference speed of 97.3 FPS. This approach significantly boosts detection accuracy and real-time performance in coal mine conveyor belt foreign object detection.

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Data availability

No datasets were generated or analysed during the current study.

Abbreviations

DLEA:

Dual-model low-light enhancement algorithm

LDGEM-Net:

Lightweight dynamic group efficient module network

EAA:

Efficient additive attention

CIoU:

Complete intersection over union

GIoU:

Generalized intersection over union

DIoU:

Distance intersection over union

MPDIoU:

Mean projected distance intersection over union

EIoU:

Efficient intersection over union

SIoU:

Self-consistency intersection over union

YOLO:

You only look once

R-CNN:

Region-based convolutional neural network

DUAL:

Dual illumination estimation

LIME:

Lowlight image enhancement

StarNet:

Star network

DGSM:

Dynamic group convolution shuffle module

DGST:

Dynamic group convolution shuffle transformer

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Acknowledgements

Special thanks to the Intelligent Detection and Pattern Recognition Research Center of China University of Mining and Technology for providing the dataset for this study. We would also like to thank every reviewer and editorial team member for their hard work and team support.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 52174141, 62105004; the Anhui Mining Machinery and Electrical Equipment Coordination Innovation Center, Anhui University of Science and Technology, grant number KSJD202304; the Anhui Digital Agriculture Engineering Technology Research Center Open Project of China, grant number AHSZNYGCZXKF021;the Graduate Innovation Fund Project of Anhui University of Science and Technology, grant number 2024cx2067, 2024cx2064; the College Student Innovation and Entrepreneurship Fund project of China, grant number 202210361053, 202310361037.

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YH and LW wrote the main text of the manuscript, and the other authors reviewed the manuscript.

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Correspondence to Lei Wang.

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Hong, Y., Wang, L., Su, J. et al. Enhanced foreign body detection on coal mine conveyor belts using improved DLEA and lightweight SARC-DETR model. SIViP 19, 349 (2025). https://doi.org/10.1007/s11760-025-03922-1

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