The work aims to realize efficient inspection of wear and corrosion defects present in wire ropes by developing a high-performance and low-cost image processing technology-based defect rocognition method of wire ropes. Gray area features of the wire ropes were firstly extracted by neighborhood average algorithm. Then a entropy-based statistical function was established to describe and quantize grayscale distribution features and grayscale fluctuation features. On this basis, no-defective product, corrosion defect and wear defect samples were selected to quantize and extract regional grayscale, grayscale distribution and grayscale fluctuation features. The distribution of different types of sample in 3-dimensional characteristic space was observed to have obvious distinguishability. Based on this feature, corrosion and wear defects were detected and recognized by setting 3-dimensional characteristic threshold. The experimental result demonstrates that the image processing-basedinspecting method proposed in this paper can recognize corrosion and wear defects efficiently and accurately and is of both academic value and practical meanings, hence it is very fit for real time inspection.