Mine Vehicle Loading Recognition System Based on Image Recognition
At present,many systems of mine construction field in China have problems of imperfect functions,poor operability,low degree of intelligence,and the data cannot be deeply used.The image recognition technology is introduced to identify the mine vehicle loading so as to improve the separation efficiency,which is a reliable way to solve the above problems.Firstly,this paper evaluates the actual performance of four types of image recognition algorithms such as Faster R-CNN,SSD,YOLO,and RetinaNet in vehicle loading classification comprehensively,and it is found that YOLO model is the most suitable.Secondly,the feature extraction network of YOLO is replaced with a lightweight MobileNet V3 network,and the size of optimized model becomes the 1/5 of the original size on the basis of the guaranteed accuracy.And it realizes effective identification and classification in different environments.Finally,it designs the human-computer interaction interface and touch screen is equipped,realizing a set of complete and intelligent recognition system for mining vehicle loading.
万方数据
维普
