Video real-time detection of bulk material accumulation on belt conveyor

The non-contact bulk material accumulation detection method has problems, such as slow detection speed, low detection precision in image fuzzy scene, large memory requirement of deep learning model. In order to solve the above problems, a video real-time detection method of bulk material accumulation on belt conveyor based on lightweight Mask-RCNN (mask region-based convolutional neural network) is proposed. Firstly, the collected image is preprocessed by the dark channel prior algorithm to reduce the image fogging phenomenon caused by dust in the transportation and loading process and improve the image edge features. The traditional Mask-RCNN backbone network ResNet can not meet the requirement of real-time detection of bulk material accumulation on an embedded platform. In order to solve this problem, the defogging preprocessed image is input into the backbone network based on MobileNetV2 + feature pyramid network (FPN) for feature extraction. The feature graph is generated. The backbone network is designed to be lightweight. The backbone network is deployed on the embedded platform to collect image data in real-time for instance segmentation. In order to find the edge of the segmented object more accurately, a method of adding edge loss in the mask branch of traditional Mask-RCNN is proposed. The mask is generated by using full convolutional network layer. The edge loss function is constructed by combining the Scharr operator. The segmentation image is obtained by fusing object classification, bounding box regression and semantic information. Finally, the bulk material accumulation detection is realized by judging whether the pixel value in the bulk material accumulation mask exceeds a preset threshold value. The experimental results show that the memory requirement of the proposed method is reduced to 1/5 of that of the model taking ResNet 101 as the backbone network. The average precision mean value after image defogging pre-processing is increased by 8%. The average detection time of one image is 0.56 s, the detection precision can reach 91.8%.