Wire rope damage detection based on a uniform-complementary binary pattern with exponentially weighted guide image filtering

Abstract

In response to the problem of unclear texture structure in steel wire rope images caused by complex and uncertain lighting conditions, resulting in inconsistent LBP feature values for the same structure, this paper proposes a steel wire surface damage recognition method based on exponential weighted guided filtering and complementary binary equivalent patterns. Leveraging the phenomenon of Mach bands in vision, we introduce a guided filtering method based on local exponential weighting to enhance texture details by applying exponential mapping to evaluate pixel differences within local window regions during image filtering. Additionally, we propose complementary binary equivalent pattern descriptors as neighborhood difference symbol information representation operators to reduce feature dimensionality while enhancing the robustness of binary encoding against interference. Experimental results demonstrate that compared to classical guided filtering algorithms, our image enhancement method achieves improvements in PSNR and SSIM mean values by more than 32.5% and 18.5%, respectively, effectively removing noise while preserving image edge structures. Moreover, our algorithm achieves a classification accuracy of 99.3% on the steel wire dataset, with a processing time of only 0.606 s per image.