GAO R X, DU Y B, CHANG J H.Study on lightweight coal gangue detection method based on improved YOLOX-S[J].Journal of Henan Polytechnic University(Natural Science) ,2024,43(4):133-140.

doi:10.16186/j.cnki.1673-9787.2022070020

Received:2022/07/10

Revised:2023/03/17

Published:2024/07/05

Study on lightweight coal gangue detection method based on improved YOLOX-S

GAO Ruxin1,2,3, DU Yabo1,2,3, CHANG Jiahao1,2,3

1.School of Electrical Engineering and Automation，Henan Polytechnic University，Jiaozuo  454000，Henan，China;2.Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment，Jiaozuo  454000，Henan，China;3.Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment， Jiaozuo  454000，Henan，China

Abstract: Objectives  To explore the influence of the existing machine vision-based coal gangue detection method in the model parametric quantity，the amount of computation on the detection speed and deployment of embedded devices，  Methods a lightweight gangue detection model based on the improvement of the anchor-free YOLOX-S was proposed.By collecting and sorting samples of coal gangue at the site，the model could extract more real coal gangue feature informations to ensure the effect of coal gangue detection in the actual environment and adapt to the actual production environment.Combined with CSPNet，the input feature map was split into two branches to realize richer gradient combinations and to reduce the computational amount of the model；then in one of the branches，Ghost lightweight convolution was used to generate a set of feature maps through a small amount of conventional convolution to reduce the computational amount and the number of parameters for the first time，and then on the basis of this feature map after a simple linear change operation，a new set of feature maps were generated，and finally the two sets of feature maps were combined to form a new set.Then，based on this feature map，a new set of feature maps was generated by a simple linear change operation，and finally，the two sets of feature maps were fused，which reduced the demand for computational resources，and at the same time，achieved the same feature extraction effect as that of the conventional convolution.The LeakyReLU activation function was introduced to alleviate the effect of the vanishing gradient of the model，and extracted more feature informations in a deeper and more.Finally，the features of the two branches were fused，which ensured a higher detection accuracy and improved the detection speed of the model.The CIOU Loss （complete IOU loss） was used to optimize the target bounding box regression loss function，which made the model regression loss converge faster and improved the model target localization ability.  Results Compared with the original model，the improved model ensured a higher average accuracy of 90.51% of the average value of the case，the model parameters were reduced by 47%，the computation amount was reduced by 49%，and the detection speed reached 50 FPS.  Conclusions The lightweight coal gangue detection model made the intelligent coal gangue detection in the actual production environment have a certain application prospect.

Key words:coal gangue detection;YOLOX-S;lightweight;object localization;detection speed