Research on lightweight coal and gangue target detection method

Abstract

In order to solve the problems of low precision, poor real-time performance and easy missing detection of small targets in the current deep learning-based coal and gangue target detection methods, the SSD model is improved by using lightweight network, self-attention mechanism and anchor frame optimization method to construct Ghost-SSD model, and then a lightweight coal and gangue target detection method is proposed. The Ghost-SSD model is based on the SSD model, and the GhostNet lightweight characteristic extraction network is used to replace the main network layer VGG16 so as to improve the detection speed of coal and gangue targets. In order to solve the problem that the shallow characteristic map contains more background noise and insufficient semantic information, the self-attention module is introduced to enhance the characteristics of the shallow characteristic map and increase the focus on the foreground region. Moreover, the dilated convolution is applied to increase the receptive field of the shallow characteristic maps and enrich the semantic information of the shallow characteristic maps. The K-means algorithm is used to cluster the anchor frames, optimize the size of the anchor frame, and further improve the precision of coal and gangue target detection. The experimental results show that when the Ghost-SSD model is applied in coal and gangue target detection, the mean average precision is 3.6% higher than that of the SSD model, the detection speed is increased by 75 frames/s, and the detection precision and speed are better than that of the Faster-RCNN and Yolov3 models. Moreover, the model has a good detection effect on small coal and gangue targets.

Keywords

• coal and gangue separation; coal and gangue identification; coal and gangue target detection; self-attention mechanism; ssd model; ghostnet; anchor frame clustering optimization