As large-scale infrastructure gradually ages, hundreds of existing bridges require regular inspections to ensure structural safety. While many researchers have proposed deterioration detection methods based on computer vision and deep learning—which can detect deterioration at the image level—no effective approach has yet been developed that integrates 3D reconstruction technology to achieve spatial localization and area quantification. To address this, this study proposes a two-part automated inspection workflow for the classification, localization, and measurement of internal deterioration in box-girder bridges. In the first part, the camera system is calibrated using an indoor calibration scene, and images are captured inside the box-girder. A 3D model is constructed using Structure from Motion (SfM) algorithms, and a cylindrical projection unfolded map is generated. In the second part, a boundary-aware model—modified from DeepV3+—is used to perform pixel-level deterioration detection and classification on the unfolded map. Experimental results demonstrate that the system can generate scale-corrected cylindrical unfolded maps from 3D models with sub-millimeter scale accuracy (0.105 mm), effectively transforming complex 3D inspection tasks into measurable and analyzable 2D images. The model achieved an overall mean Intersection over Union (mIoU) of 65.11% across five classes (four deterioration types and the background), representing a 7.54 percentage point improvement over the original DeepV3+. The research results validate the effectiveness of the proposed workflow in enhancing detection efficiency and objectivity for box-girder bridge maintenance.