Damage detection method using object detection with deep learning and its application to 360-degree images

Abstract

While studies of damage detection from planar images have reported high accuracy, there have been issues with the comprehensiveness and efficiency of photography. In this study, we addressed this issue by utilizing 360-degree images to construct a detection and segmentation model for exposed rebar and evaluate its detection accuracy. First, we evaluated detection accuracy for planar images. The segmentation model demonstrated stable performance with an average IoU of 0.746, and the detection model demonstrated stable performance with an average Recall of 0.858, but the average Precision was only 0.765. Furthermore, we attempted inference by applying distortion correction using Cubemap transformation to 360-degree images, but the Recall was only 0.326, revealing that issues with detection accuracy remain. In the future, we plan to expand the training data for 360-degree images and develop a GUI app for inference, with the aim of practical application of image recognition to provide visual support to inspection personnel.