A Sensor Based Waste Rock Detection Method in Copper Mining Under Low Light Environment

TL;DR

This paper introduces a deep learning method to detect waste rock in copper mining under low light, improving sorting accuracy and efficiency.

Contribution

A novel deep learning algorithm with modules for low-light adaptation and detection accuracy enhancement in copper mining waste sorting.

Findings

The proposed algorithm achieved an mAP@0.5 of 0.957 and mAP@0.5:0.95 of 0.689.

The method outperformed advanced methods by 1.9% and 8.6% in mAP metrics.

Illumination Adaptive Transformer and Local Enhancement-Global Modulation modules improved performance in low-light conditions.

Abstract

During production, copper mining could generate substantial waste rock that impacts land use and the environment. Advances in deep learning have enabled efficient, cost-effective intelligent sorting, where vision sensor performance critically determines sorting accuracy and efficiency. However, the sorting environment of copper mine waste rock is inherently complex, particularly within the conveyor belt section of the sorting machine, where insufficient and uneven lighting significantly impairs the performance of vision-based detection systems. To address the above challenges, a deep-learning-based copper mine waste rock detection algorithm under low-light environments is proposed. Firstly, an Illumination Adaptive Transformer (IAT) module is added as a preprocessing layer at the beginning of the Backbone to enhance the brightness of the images acquired by the vision sensor. Secondly, a Local Enhancement-Global Modulation (LEGM) module is integrated after the A2C2f and C3k2 modules in the Neck to enhance the detection accuracy. Finally, to further improve the model performance, MPDIoU is introduced to optimize the original loss function CIoU. As a result, the proposed algorithm achieved an mAP@0.5 of 0.957 and an mAP@0.5:0.95 of 0.689, outperforming advanced methods by 1.9% and 8.6%, respectively.

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