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This research aims to enhance the accuracy and speed of coal gangue detection and identification using deep learning methods.

April 15, 2026ProcessesOpen Access

Research on Coal Gangue Detection and Identification Method Based on Improved YOLOv7

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Research reports enhanced coal gangue detection efficiency using a modified YOLOv7 model, indicating significant advancements in sorting accuracy.

This research aims to enhance the accuracy and speed of coal gangue detection and identification using deep learning methods.
Developed an improved YOLOv7 model tailored for high computational efficiency on common GPU devices.
Integrated FasterNet to refine the backbone network of YOLOv7, minimizing feature redundancy.
Implemented VoVGSCSP to optimize the efficient layer aggregation network in YOLOv7 for improved detection speed.
Achieved a similar mAP0.5 to YOLOv7 at a belt speed of 0.6 m/s.
Increased frames per second (FPS) from 9 to 18.
Attained a coal gangue sorting rate of 91.1% with a misselection rate of only 4.8%.

Hou et al. (2026) studied this question.

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Research reports enhanced coal gangue detection efficiency using a modified YOLOv7 model, indicating significant advancements in sorting accuracy.