The field of infrastructure inspection and defect detection is rapidly advancing with the development of innovative AI models and computer vision techniques. Researchers are focusing on creating synthetic benchmarks and robust perspective correction methods to improve the accuracy of crack detection and tracking in real-world scenarios. The use of deep learning frameworks, such as ensemble models and residual U-Nets, is also gaining traction for image-based structural crack detection and solar panel classification. These advancements have the potential to significantly enhance the efficiency and reliability of infrastructure inspection and maintenance tasks. Noteworthy papers include the introduction of the CERBERUS benchmark for evaluating AI models for defect detection, and the development of a physics-informed alignment framework for accurate geometric correction in crack evolution tracking. The use of hybrid ensemble learning techniques for solar panel classification and the comparison of advanced computer vision models for container damage detection also demonstrate the innovative approaches being explored in this field.