Fire hose defect detection method and system based on computer vision

Abstract

The invention relates to the technical field of fabric defect detection, in particular to a fire hose defect detection method and system based on computer vision. The method determines the abnormal region through the optical flow information. And converting the texture edge in the abnormal region into a Hough space to obtain the form information of the texture edge. Clustering is carried out according to the Hough curve form in the Hough space, and a first cluster is obtained; and obtaining a first defect area according to the number of the abnormal samples in the first cluster. And determining a second defect area according to the abnormal distance between the parallel texture edges corresponding to the first cluster. According to the method, the morphological information of the texture edges is obtained through Hough space conversion, and defect detection is accurately carried out according to the state between the edges.

Description

technical field

[0001] The invention relates to the technical field of fabric defect detection, in particular to a method and system for detecting fire hose defects based on computer vision. Background technique

[0002] Traditional fire hoses are rubber-lined and linen braided on the outside. At present, the defects of fire hose mainly exist in the fabric layer of the hose, and the fabric layer is required to be woven evenly, the surface is clean, and there is no double warp skipping, double warp breakage, weft skipping and scratches.

[0003] It is time-consuming, laborious, and inefficient to manually complete the defect detection of the outer surface water belt fabric layer. Therefore, in the prior art, computer vision technology can be used to determine defects by detecting pixel differences in fabric pictures. However, because the texture of the fabric layer of the fire hose is too dense, it will be affected by a large amount of texture information when detecting defe...

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