基于残差网络的运煤皮带异物分类方法

Foreign body classification method of coal belt based on residual network

在煤炭运输的过程中往往会有异物划伤或撕裂运输皮带,造成出煤口堵塞等安全事故。因此需要及时识别出运煤皮带上的异物并对其分类,以此进行预警、分选并控制来降低事故发生几率。针对多数分类网络存在计算参数量大、分类准确率不高等问题,提出一种基于残差网络构建的运煤皮带异物分类网络。该网络采用多个小卷积层代替第一层的7×7卷积以增强对局部特征的捕捉能力,并加入批标准化层和ReLU激活函数,使网络更快收敛的同时增强网络的非线性能力;在残差块中使用深度可分离卷积代替普通卷积,大幅降低网络的参数量和计算量,加快模型推理速度;在残差块中引入CBAM注意力机制,增强网络对通道特征和空间特征的学习能力,减弱无用背景信息对模型的影响,将注意力集中到运煤皮带区域;将深层特征与部分浅层特征融合,提升对锚杆类小目标异物的识别率。该网络在自建的矿井数据集上的精确率达到了91.4%,比改进前的网络提高了4.7%,召回率达到了91.2%,比改进前的网络提高了5.8%,计算量降低了20%,参数量降低了31%。结果表明,构建的网络准确率更高,更加轻量化,训练速度更快,实时性更强。

In the process of coal transportation,there are often foreign bodies scratching or tearing the transportation belt,resulting in safety accidents such as coal outlet blockage.Therefore,it is necessary to identify and classify the foreign bodies on the coal conveyor belt in time,so as to carry out early warning,sorting and control to reduce the probability of accidents.Aiming at the problems of large amount of calculation parameters and low classification accuracy in most classification networks,a classification network of coal belt foreign bodies based on residual network is proposed.The network uses multiple small convolution layers instead of the 7×7 convolution of the first layer to enhance the capture ability of local features and adds BN layer and ReLU activation function to make the network converge faster and enhance the nonlinear ability of the network.In the residual block,the depthwise separable convolution is used instead of the ordinary convolution,which greatly reduces the parameter quantity and calculation amount of the network and speeds up the model inference.After adding the CBAM attention mechanism to the convolution layer in the residual block,the network′s ability to learn channel features and spatial features is enhanced,the influence of useless background information on the model is weakened,and the attention is focused on the coal belt area.The deep features are fused with some shallow features to improve the recognition rate of small target foreign bodies such as anchors.The accuracy of the network on the self-built mine data set reached 91.4%,which was 4.7%higher than that of the improved network.The recall rate reached 91.2%,which was 5.8%higher than that of the pre-improved network.The calculation amount was reduced by 20%,and the number of parameters was reduced by 31%.The results show that the constructed network has higher accuracy,lighter weight,faster training speed and stronger real-time performance.