Research Of Coal And Gangue Object Detection Method Based On Deep Learning

With the gradual improvement of the intellige nt requirements in coal and gangue sorting system,researchers have proposed automatic coal and gangue separating methods,including ray method,mechanical washing method,and image recognition method.The former two will cause environmental pollution and cost too much,so the automatic separating of image-based coal and gangue recognition has become the first choice of researchers.The difficulty of this method is how to accurately complete the object,detection task of coal and gangue.Deep learning has shown great potential in various fields,this paper applies generative Adversarial network and one-stage object detection model to coal and gangue object detection,aiming to improve detection accuracy and speed.At present,there is no publicly available coal and gangue data set,and the number of collected coal gangue images is limited.The traditional data set expansion method cannot effectively improve the diversity of samples.Coal and gangue object detection is a real-time detection task,which requires high detection speed.The two-stage object detection model has high accuracy,but the speed is difficult to meet the requirements of real-time detection.In response to the above problems,this paper mainly conducts the following research work:First,in the expansion of coal and gangue data set,the method based on generative adversarial network is studied,aiming at the problem of the instability of the original GAN training,this paper uses WGAN and DCGAN to expand the data set,and conduct a comparative experiment.Second,the one-stage object detection model SSD can ensure high detection speed while maintaining high accuracy.However,it's backbone network VGG-16 has many parameters.This article uses SSD-MobileNet as the coal gangue detection model and performs The improvement is that the 19 × 19 shallow feature layer is removed when making predictions,so that the number of a prior bounding box is reduced from 1917 to 834.Experimental results show that the FID score of images generated by DCGAN is significantly lower than that of WGAN generated images,indicating that the quality of images generated is better than that of images generated by WGAN,compared with traditional methods,DCGAN-based data set expansion significantly improves the mAP value of the object detection model.The mAP value of the improved SSD-MobileNet model is only reduced by 0.79%,and the average detection time of a single image is reduced from 0.06s to 0.03s,which effectively reduces the detection time while ensuring the detection accuracy.