Foreign object recognition in coal conveyor belt based on multi-attention fusion network
In order to slove the problems of large network parameters and low recognition accuracy of the existing foreign object recognition algorithms for coal conveyor belt,and to avoid safety hazards just in time caused by large blocks of coal,gangue,anchor rods,etc.,a foreign object recognition method for coal conveyor belts based on a multi-attention fusion network is proposed.The low illumination image processing algorithm is adopted to preprocess the dataset.A fused local attention residual block is cho-sen as the basic feature extraction unit with deformable convolution with additional offsets integrated in-to the residual block to enhance the description of irregular features.The global feature map is pro-cessed with expectation maximization using an attention mechanism.The results show that the recogni-tion accuracy rates on the Cifar10 dataset and the foreign object recognition dataset for mining belt transmission are 93.7％and 84.8％,respectively.Compared with algorithms such as ShufflenetV2,MobileNetV2,ResNet 50,ResNet 110,and Darknet 53,the proposed method increases the recognition accuracy rates by 4.7％,3.9％,0.4％,0.5％and 1.7％,respectively.Compared with algorithms such as ResNet50 and ResNet110 with similar recognition accuracy rates,it reduces the network pa-rameters and computational complexity significantly.This recognition method can quickly identify for-eign object in coal conveyor belt and has a high recognition accuracy rate,which has reference signifi-cance for ensuring the safe operation of coal mine transportation systems.
国家科技期刊平台
NSTL
万方数据
