Computer vision-based intelligent detection method for the residual capability of energy dissipators in flexible protection systems

Abstract

A residual capability intelligent detection method based on computer vision is proposed to address the issues of low efficiency, poor accuracy, and high danger in manual measurement of energy dissipators in flexible protection systems. The proposed method first establishes a binary semantic segmentation dataset for energy dissipators and trains a salient object detection deep neural network to segment the energy dissipator binary map; Then, it uses morphological image processing and contour detection to calculate the residual capability automatically. U²-Net, U²-Netp, and BASENet were trained and compared by a dataset with 500 ring-type energy dissipator images. The proposed method was validated through a quasi-static tensile test and a full-scale impact test. Compared with the most accurate integration calculation method, the error of the proposed method does not exceed 3 %, and the efficiency is improved by about 25 times compared to the most commonly used manual detection method.