Research of coal and gangue identification and positioning method at mobile device

A method of identifying and positioning coal and gangue on mobile devices is provided to address the difficulties of high complexity and difficult deployment of existing machine vision algorithms. Combining CSPDarknet53 and the GhostNet embedded with efficient channel domain attention to build a lightweight feature extraction network and using the Meta-ACON activation function to adapt the network’s nonlinearity at each layer. The detection head’s expression capability is enhanced using a decoupled head. The actual position coordinate relationship equation of coal and gangue is deduced by borrowing the center point object box positioning principle. Build an experimental setup, collect and construct coal and gangue datasets, and use Mixup data augmentation to improve the network’s ability to position coal and gangue for stacking cases. The models are trained, and tested for different lighting conditions, sizes, and stacking cases, then deployed on the mobile device. The results show that the model has the highest detection accuracy and can correctly identify and position both coal and gangue under complex conditions with a high confidence value. The FLOPs is 76.83% lower compared to the original network YOLOv5s. The mAP reaches 0.996 and the FPS is 40.11. The inference time on the mobile device is reduced to 228 ms, which basically meets the speed requirement of coal gangue identification and positioning.