GSSA-YOLOM-Based Foreign Object and Conveyor Belt Deviation Detection

TL;DR

This paper introduces a new algorithm for detecting foreign objects and belt deviations in coal conveyors, improving safety and efficiency.

Contribution

The novel GSSA-YOLOM algorithm balances multiple tasks with improved accuracy and reduced computational complexity.

Findings

The GSSA-YOLOM model improves mAP@50 by 3.4% compared to the baseline.

The model reduces parameters by 27%, making it suitable for edge device deployment.

The algorithm effectively detects foreign objects and belt deviations in coal conveyance systems.

Abstract

The safety of belt conveyor operation is of great importance during coal conveyance. This paper proposes a multi-task-based GSSA-YOLOM algorithm for monitoring the state of belt conveyors, which utilizes segmentation head to detect foreign objects and belt deviation, thereby balancing the trade-offs among multiple tasks. The detection neck is responsible for multi-scale feature fusion by incorporating the Asymptotic Feature Pyramid Network (AFPN) to achieve enhanced spatial perception. Then, Groupwise Separable Convolution (GSConv) is further introduced to simplify the network architecture, reducing computational complexity while maintaining sufficient detection accuracy for edge device deployment. Moreover, the SlideLoss and Soft-NMS functions are integrated to reduce the rate of false positives and missed detections. Comparison experiments were conducted, and the results indicate that the proposed GSSA-YOLOM model can improve mAP@50 by 3.4% compared with the baseline model while reducing the number of parameters by 27%, thereby satisfying coal mine safety monitoring requirements.

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