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Mining belt foreign body detection method based on YOLOv4_GECA model.

Dong Xiao^1,2, Panpan Liu^3,4, Jichun Wang^5,6

¹Information Science and Engineering School, Northeastern University, Shenyang, 110004, China. xiaodong@ise.neu.edu.cn.

Scientific Reports

|June 1, 2023

Summary

Foreign object detection in mining belts is crucial for safety and efficiency. The proposed YOLOv4_GECA method achieves 90.1% accuracy and 30ms detection time, meeting real-time mining safety requirements.

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Area of Science:

Engineering
Computer Science
Artificial Intelligence

Background:

Mining belt conveyors are susceptible to foreign object intrusion, posing risks to equipment and personnel.
Early detection of foreign objects is vital to prevent production delays and safety hazards in mining operations.

Purpose of the Study:

To develop an effective method for detecting foreign objects in mining belt conveyor systems.
To enhance the accuracy and real-time performance of foreign object detection.

Main Methods:

Implementation of the YOLOv4_GECA model, incorporating the GECA attention module for improved feature extraction.
Utilizing restart cosine annealing learning rate decay to optimize detection performance.
Creation of a dedicated foreign body detection dataset from a gold mine site.

Main Results:

The YOLOv4_GECA method achieved an average detection accuracy of 90.1%.
A recall rate of 90.7% was recorded.
The model demonstrated an average detection time of 30 milliseconds, ensuring real-time capability.

Conclusions:

The YOLOv4_GECA method effectively detects foreign objects in mining belt transportation.
The proposed approach meets the stringent requirements for both accuracy and real-time performance in industrial mining settings.