Research On Coal Gangue Detection Based On Deep Learning

The sorting of coal gangue is an important segment to ensure clean and efficient utilization of coal.In order to conform with the development trend of automation,intelligence of coal gangue sorting technology,a coal-gangue sorting robot has been designed.It uses image object detection technology to identity and locate coal gangue,and uses mechanical paws to grasp gangue to separate coal gangue.Coal gangue image detection is one of the key techniques for automatic separation of the coal-gangue sorting robot.Traditional detection methods mainly focus on the research of coal gangue image recognition,which has the problems of complex image processing and limited application scenarios of recognition algorithms.In view of those problems and based on previous researches,this paper proposes a method of using deep learning to detect coal gangue to solve the problem of coal gangue recognition and positioning,and provide basis for the coal-gangue sorting robot to separate coal and gangue.The main research contents are as follows:At present,there is a lack of publicly available coal gangue data set.According to image features and format of the PASCAL VOC,coal gangue images in different scenarios are collected,and the images are preprocessed by means of screening,sample augment,etc.On this basis,the processed images are labeled and then a coal gangue data set is created.According to the selection requirements of coal gangue detection model,this paper proposes a coal gangue detection method based on Faster R-CNN and gives the overall process of coal gangue detection.In order to reduce training time and resources of the coal gangue detection model,the transfer learning is used to pre-train the model,then the coal gangue detection model based on Faster R-CNN is trained with the coal gangue data set.Compared with the SSD gangue detection model by experiments,this paper verifies the feasibility and reliability of the coal gangue detection method.By studying the detection principle of Faster R-CNN to first generate region proposal and then classify and positioning,it is proposed to use ResNet-50 replace VGG of the Faster R-CNN algorithm as the feature extraction network,and use Soft-NMS instead of NMS to filter the border,to improve the detection performance.Setting different types and numbers of anchor,and it is verified through experiments that appropriate reduction of types and numbers of anchor can improve the detection speed while ensuring better detection accuracy.In the coal-gangue sorting robot system,a visual recognition module is built,which can realize functions of collection of detection data,image preprocessing,online detection of coal gangue,detection effect display,etc.The trained coal gangue detection model is used to online test 254 coal gangue samples in different environments,and improvement measures are proposed for the false detection and missed detection,and the target gangue is positioned at the center of the rectangular detection frame.The experimental results show that the model proposed in this paper has achieved good detection effect in the application of the coal gangue sorting robot system,which meets the research expectations.