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Updated: Feb 28, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

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

Zuguo Chen^1,2,3, Jiayu Liu², Yimin Zhou⁴

¹Sanya Institute of Hunan University of Science and Technology, Sanya 572024, China.

Sensors (Basel, Switzerland)

|February 27, 2026

Summary

This summary is machine-generated.

A new GSSA-YOLOM algorithm enhances belt conveyor safety in coal mines by detecting foreign objects and belt deviation. This AI model improves detection accuracy while reducing computational load for reliable monitoring.

Keywords:

belt deviation detection conveyor belt foreign object detection multi-task learning

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Fabrication and Operation of a Nano-Optical Conveyor Belt

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Area of Science:

Industrial Safety
Artificial Intelligence
Machine Learning

Background:

Belt conveyor safety is critical in coal mining operations.
Current monitoring systems may face challenges in detecting multiple issues simultaneously.

Purpose of the Study:

To propose a novel multi-task algorithm for belt conveyor state monitoring.
To enhance the detection of foreign objects and belt deviation.

Main Methods:

Developed a GSSA-YOLOM algorithm integrating segmentation heads for multi-task detection.
Employed Asymptotic Feature Pyramid Network (AFPN) for multi-scale feature fusion.
Utilized Groupwise Separable Convolution (GSConv) for architectural simplification and SlideLoss/Soft-NMS for improved detection accuracy.

Main Results:

The GSSA-YOLOM model improved mean Average Precision at 50% IoU (mAP@50) by 3.4% compared to the baseline.
Reduced the number of model parameters by 27%, indicating computational efficiency.
Demonstrated effective detection of foreign objects and belt deviation.

Conclusions:

The GSSA-YOLOM algorithm offers a robust solution for coal mine safety monitoring.
The model balances detection accuracy with reduced computational complexity, suitable for edge devices.
This approach satisfies critical coal mine safety monitoring requirements.