Detection and measurement of coal gangue based on deep learning
In view of the difficulty for real-time measurement of coal gangue on the belt conveyor in coal mine,a dynamic measurement method of belt Gangue based on Yolact algorithm is proposed in combination with image processing technology and deep learning technology.Firstly,the image is preprocessed,including filtering and illumination enhancement.Then,the lightweight residual structure is used as the feature extraction backbone of Yolact algorithm to segment the belt gangue in re-al time.Finally,binarize the segmentation results of gangue,introduce the open source cross platform computer vision library Opencv,and calculate the specific quantity and area of gangue using pixel threshold.The feasibility of the gangue metering algorithm is verified by building a belt gangue sorting device.The experimental results show that the proposed method can effectively learn the characteristics of gangue.The accuracy of measuring the area and position of gangue on the belt by the network is 94.66％,and the detection speed of the network is 30.72 FPS.The proposed method can effectively measure the gangue on the coal mine belt.
万方数据
维普
