The field of computer vision is making significant strides in infrastructure inspection, with a focus on developing innovative methods for automated damage detection and object recognition. Recent research has explored the application of deep learning techniques to detect structural damage in concrete structures, such as exposed steel reinforcement, and to recognize objects in civil engineering applications, including tunnel segment crack detection and construction PPE detection. These advancements have the potential to improve public safety and emergency response, as well as enhance construction safety monitoring and infrastructure inspection. Notable papers in this area include: DINO-YOLO, which introduces a hybrid architecture combining YOLO with DINO self-supervised vision transformers for data-efficient object detection, and a paper on deep learning-based automated damage detection in concrete structures using images from earthquake events, which demonstrates the effectiveness of a hybrid framework for automatically determining damage levels from input images.