Image detection method for compression defects in tension line clamps of transmission lines based on RetinaNet algorithm

When conducting radiographic testing on tension clamps of transmission lines, the projection process of image features ignores the impact of image clarity on the detection results, which can lead to lower AP values in the detection results. Therefore, a method for detecting compression defects in tension line clamps of transmission lines based on RetinaNet algorithm was proposed. This method determined the grayscale level of the compressed image of the tension wire clamp, calculated the optimal segmentation threshold to retain the detection target contour, compensated for the difference in inclination angle of the contour to improve its clarity, and reconstructed the defect image through two-dimensional inverse Fourier transform. The RetinaNet algorithm was introduced to fuse and extract image features, deconstructed the compressed part of the tension wire clamp, and calculated the proportion of defect features to obtain the defect detection result. The experimental results showed that the defect detection results obtained by applying the proposed method had a high AP value and detection accuracy, which met the practical needs of quality inspection for tension line clamps in transmission lines.