Gong-kuang zidonghua (Apr 2023)
An improved tiny YOLO v3 rapid recognition model for coal-gangue
Abstract
The traditional coal gangue sorting methods have low efficiency, significant safety hazards, and limited application scope. The existing machine vision-based coal gangue image recognition methods are difficult to balance model recognition speed and accuracy. And the methods do not comprehensively consider the impact of different input image sizes, low important channel weights, and large convolution parameters on model precision. In order to solve the above problems, an improved tiny YOLO v3 coal gangue rapid recognition model is proposed based on the tiny YOLO v3 model. Firstly, a spatial pyramid pooling (SPP) network with multiple convolutional kernels combined pooling is introduced in the tiny YOLO v3 model to ensure that the input feature maps can be processed to a fixed size before being output. Secondly, a squeeze-and-excitation (SE) module with adjustable RGB channel weights is introduced to enhance the connections between the channels in the previous layer feature maps. It emphasizes the differences between the feature values of the interested channels and the features of different targets. It ensures the capture of key information and network sensitivity. Finally, the dilated convolution containing zero weight points is introduced to replace part of the convolution layer in the tiny YOLO v3 model. Under the premise of not adding model parameters, multi-scale context information can be captured to expand the receptive field and improve the calculation speed of the model. This model is compared with the tiny YOLO v3 model, Faster RCNN model, and YOLO v5 series models respectively. The results show the following points. ① Compared with tiny YOLO v3, the improved tiny YOLO v3 coal gangue rapid recognition model has significantly improved recognition accuracy and speed. ② Compared with Faster RCNN, the improved tiny YOLO v3 coal gangue rapid recognition model has reduced training time by 65.72%, increased recognition precision by 11.83%, increased recognition recall by 0.5%, and increased model mean average precision (mAP) by 3.02%. ③ Compared with the YOLO series model, the improved tiny YOLO v3 coal gangue rapid recognition model has a significant increase in recognition speed while maintaining the advantage of recognition precision. The results of the ablation experiment show that the improved tiny YOLO v3 coal gangue rapid recognition model has a recognition accuracy of 99.4%. It is 4.9% higher than the tiny YOLO v3 model added with the SPP network. The time to test each image is 12.5 ms, which is 1 ms less than the tiny YOLO v3 model added to the SPP network.
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