基于改进YOLOv8的煤矿皮带异物检测方法OA

Objective On the transport belt used for normal coal flow,large coal gangue,anchor rods,and other foreign objects can be present.When large coal gangue or other foreign objects accumulate at the coal drop port,issues such as coal stacking and coal blockage occur.Anchor rods and other foreign objects can become entangled with transport belt components,causing surface scratches or even severe belt tearing,which seriously af-fects the normal coal flow transport.Deep learning methods previously applied demonstrate inferior baseline network performance compared to the YOLOv8(You Only Look Once)model and fail to incorporate targeted lightweight optimization for edge deployment scenarios.Currently,computer vision-based detection methods do not achieve performance improvements over the YOLOv8 model in coal mine target detection tasks.Therefore,this study proposes a foreign object detection method for coal mine conveyor belts,YOLOv8‒SPCD,which is developed based on an improved YOLOv8 framework. Methods The YOLOv8‒SPCD model introduced several key improvements to enhance the detection performance of the original YOLOv8 model.First,the coal belt foreign body dataset was constructed based on existing mine images.The labelme tool was utilized to annotate the im-age data,and the images were divided into the training set(train),validation set(val),and test set(test)based on a ratio of 8:1:1.Then,SPD‒Conv was utilized to replace the convolutional component in the Backbone,and the spatial blocks of the input feature map were rearranged into the channel dimension to increase the number of channels,reduce the spatial resolution,and retain richer information during the feature extraction stage.Next,partial convolution was introduced to improve the C2f structure in the original network.The computation of redundant feature maps was reduced,while the spatial features of the input images were still effectively extracted by applying convolution only to part of the input chan-nels.Then,a lightweight cross-scale feature fusion module(CCFM),was utilized to improve the Neck component and enhance the detection capa-bility of the model for objects at different scales.Finally,to eliminate the adverse effect of the penalty term in the original loss function on conver-gence speed and to obtain faster and more effective regression results,the improved Inner‒DIoU function was introduced to optimize the bound-ing box regression loss of the network,enabling faster convergence and more accurate localization of belt foreign bodies during training. Results and Discussions Groups 1 to 4 experiments were independent experiments in which the improved modules were modified separately on the baseline network,allowing the impact of each individual module on the baseline network to be clearly observed.In the third group of experi-ments,the CF‒Neck structure was utilized to replace the original Neck component,and the mAP value remained unchanged even though the num-ber of model parameters was reduced by 37%,indicating that CF‒Neck enhanced the detection capability of the model for objects at different scales.In the fourth group of experiments,Inner‒DIoU was utilized to replace the CIoU loss function,and the experimental indicators,such as mAP@0.5 and FPS,were improved,indicating that Inner‒DIoU effectively enhanced the fitting performance of the model.The ninth group of ex-periments corresponded to the YOLOv8‒SPCD model proposed in this study.The model weight was reduced to 43%of the baseline network,GFLOPs was reduced to 59%of the original value,mAP@0.5 was increased by 4.3 percentage points,mAP@0.5:0.95 was increased by 4.1 per-centage points,and FPS was slightly improved.The effectiveness of the proposed method for detecting foreign objects on coal mine belts was thus verified.The training loss curves of the YOLOv8‒SPCD model with Inner‒DIoU and without Inner‒DIoU were compared in this study,and the results showed that the convergence speed of the YOLOv8‒SPCD model with Inner‒DIoU was significantly faster than that of the model with-out Inner‒DIoU.The Box Loss,which measured the discrepancy between the actual boundary box and the predicted boundary box of the target object,and the Classification Loss,which measured the accuracy of the model in predicting each target category,were both significantly reduced.The distribution focal loss(DFL),which was utilized to correct errors in predicting object boundary frames,remained similar to that before modi-fication during training,indicating that the fitting performance of the proposed model on the mine image dataset was superior to that of the origi-nal model.The proposed model was also compared to mainstream target detection models such as YOLOv3-tiny,YOLOv5n,YOLOv6n,SSD,and Faster R‒CNN.The comparison results showed that the proposed model exhibited clear advantages. Conclusions The YOLOv8 model provides a feasible technical solution for detecting the presence of coal gangue,bolts,and other foreign matter during the coal conveying process on conveyor belts.The improved model integrates a series of enhancement strategies,including SPD‒Conv,PConv,the CCFM,and the Inner idea,demonstrating the broad application potential of the YOLOv8 model in coal mine target detection.This work provides a prerequisite for deployment at the mine edge.Then,the research objective is to deploy the improved model on embedded equip-ment at the mine edge end,realize practical algorithm application,and further optimize the model during the deployment process.