煤矿带式输送机异物检测

Coal mine belt conveyor foreign object detection

针对现有基于深度学习的带式输送机异物检测方法存在检测速度慢的问题,提出了一种改进YOLOv3模型,并将其应用于煤矿带式输送机异物检测。该模型以轻量化网络DarkNet22-DS作为主干特征提取网络,DarkNet22-DS利用深度可分离卷积替换标准卷积,大幅减少了网络参数,并通过复合残差块提高了特征利用效率;通过引入加权双向特征金字塔网络及双尺度输出来改进特征融合网络,提升了模型对大块异物的检测效率;采用完全交并比损失函数作为目标框回归损失函数,充分利用目标框信息间的相关性,提高了模型的收敛速度和检测精度。将改进YOLOv3模型部署在嵌入式平台Jetson Xavier NX上进行煤矿带式输送机异物检测实验,结果表明,相较于YOLOv3模型,改进YOLOv3模型权重文件大小降低了91.4%,大幅减少了模型参数,检测速度提高了16倍,达30.7帧/s,满足煤矿带式输送机异物实时检测需求。

In order to solve the problem of slow detection speed of existing deep learning based belt conveyor foreign object detection methods,an improved YOLOv3 model is proposed and applied to coal mine belt conveyor foreign object detection.The model uses the lightweight network DarkNet22-DS as the backbone feature extraction network.DarkNet22-DS replaces the standard convolution with depthwise separable convolution,which reduces the network parameters significantly and improves the feature utilization efficiency by composite residual blocks.By introducing weighted bi-directional feature pyramid networks and dual-scale output,the model improves the feature fusion network and enhances the model's detection efficiency of large foreign objects.The complete intersection ratio loss function is used as the target box regression loss function,and the correlation between the target box information is fully utilized to improve the convergence speed and detection accuracy of the model.The improved YOLOv3 model is deployed on the embedded platform Jetson Xavier NX for coal mine belt conveyor foreign object detection experiments.The results show that compared with the YOLOv3 model,the weight file size of the improved YOLOv3 model is reduced by 91.4%,and the amount of model parameters is reduced significantly.The detection speed is increased by 16 times,reaching 30.7 frames/s.The performance meets the real-time detection requirements of foreign objects in coal mine belt conveyors.