Defect detection algorithm of underground drainage pipe based on improved YOLOv5
Aiming at the problems of low efficiency and poor accuracy in the detection of underground drainage pipe de-fects,this paper proposes a defect identification method for underground drainage pipes based on the improved YOLOv5 model.Firstly,in order to improve the detection accuracy,the convolutional block attention mechanism module(CBAM)is introduced to solve the problem that some defects are too small,and a new spatial pyramid pooling module is designed to solve the problem of large differences in size,shape and color of different types of defects.Secondly,a weighted bidirectional feature pyramid net-work structure(BiFPN)is introduced in the feature fusion layer to make full use of features of different scales,which improves the feature fusion ability.Finally,aiming at the problem of blurred pictures,the histogram equalization and sharpening of the original image is carried out.The experimental results show that the proposed algorithm can well identify five kinds of defects such as obstacles,wrong mouths,cracks,tree roots and wrong mouths,and the mean average precision is 2.5%higher than the original YOLOv5 algorithm.
NETL
NSTL
万方数据
