Improved defect detection and geometric characterization of drainage pipes in YOLOv7 algorithm
Regular inspection of drainage pipes can find serious defects in time,which is of great significance to ensure the healthy operation of the drainage system and the safety of the urban environment.Aiming at the difficulty of detecting low illumination and low resolution of lower drainage pipes,an improved drainage pipeline defect detection and geometric characterization method of YOLOv7 algorithm is proposed.Firstly,the Contrast-Limited Adaptive Histogram Equalization(CLAHE)image enhancement technique is used to improve the contrast and detail of the image,so as to improve the detection network's ability to capture drain-age pipe defects.Secondly,based on the design of Drop-CA and MC modules,the YOLOv7 algorithm is improved,so that the net-work can obtain the semantic information of shallow defects,reduce the false detection rate,and improve the classification and lo-calization capabilities of the model.Finally,for the two serious defects of crack and fracture,a method is designed to quantitatively describe the geometric characteristics of the defect to evaluate the size of the defect.Experimental results show that the final aver-age accuracy of the improved network model reaches 93.3％,and the detection speed reaches 42.9 f/s.This method effectively improves the accuracy of defect detection and classification of drainage pipelines,and can effectively characterize the geometric characteristics of defects.
image enhancementdefect detectionYOLOv7 algorithmDrop-CAgeometric features
万方数据
维普
