The field of human-robot collaboration and computer vision is rapidly evolving, with a focus on developing innovative solutions for real-world problems. Recent research has explored the application of artificial intelligence and robotic technologies to improve inspection methodologies, detect anomalies, and enhance situational awareness. Notably, the integration of human-robot collaboration has shown significant improvements in inspection accuracy and reductions in human workload. Furthermore, advances in computer vision have enabled the development of efficient and accurate models for detecting abnormalities, such as concrete cracks and fatigue driving. These advancements have the potential to transform various industries, including transportation and construction, by improving safety, efficiency, and productivity. Noteworthy papers include: An Exploratory Study on Crack Detection in Concrete through Human-Robot Collaboration, which demonstrates the effectiveness of AI-assisted visual crack detection. TACR-YOLO: A Real-time Detection Framework for Abnormal Human Behaviors Enhanced with Coordinate and Task-Aware Representations, which proposes a novel framework for real-time abnormal human behavior detection. A Real-time Concrete Crack Detection and Segmentation Model Based on YOLOv11, which achieves significant performance improvements over baseline models for concrete crack detection.