Target Detection Method of Truck in Open-Pit Mine Based on Improved YOLOv3 Algorithm
As an excellent model in object detection algorithms,YOLOv3 has been widely used in many fields.An improved Yolov3 target detection algorithm for mining truck was proposed to solve the problems of complex working environment and large variety of mining truck target scale.The fourth detection scale was added to the Darknet-53 backbone network of YOLOv3,and the feature fusion was performed between the shallow network and the deep network to improve the small target detection effect.K-means algorithm was used to improve the size of the prior bounding box and obtain the most suitable prior bounding box for mining trucks.CIoU regression optimization loss function was used to improve the detection precision.The experimental results show that in the detection of mining trucks using the improved YOLOv3 model,the average detection precision reaches 96.2％,which is 2.6％higher than the original YOLOv3 model,and the target detection speed can meet the needs of real-time detection.
万方数据
维普
