<p>Bridge collisions, particularly those involving over-height vehicles, pose significant threats to public infrastructure, economic stability, and human safety. This study presents an intelligent, vision-based Bridge Collision Avoidance System (BCAS) that leverages advanced camera calibration techniques, motion detection algorithms, and real-time risk assessment frameworks to proactively detect and mitigate potential collisions. The system architecture integrates high-resolution video feeds with precise intrinsic and extrinsic camera calibration to accurately transform 2D motion into real-world coordinates. Motion detection and object segmentation are performed using a hybrid approach combining traditional background subtraction with deep learning-based models such as YOLOv11 and Vision Transformers (ViT), ensuring robustness in dynamic lighting and occlusion-prone environments. Object trajectory estimation is achieved through frame-wise velocity computation and spatial projection, enabling predictive collision path analysis. A risk evaluation model classifies threat levels using spatial thresholds, velocity vectors, and entropy-calibrated confidence scores. Real-time alerts are dispatched through low-latency edge-cloud frameworks with visual and auditory feedback to connected operators. Experimental validation across diverse scenarios—including occlusion, night conditions, and dense traffic—demonstrates superior performance in terms of accuracy (95.7%), false alarm rate (3.2%), and average system response latency (162 ms), when benchmarked against traditional rule-based and motion detection systems. This research contributes a modular, scalable, and fault-tolerant solution suitable for real-world deployment to enhance bridge safety in smart urban infrastructures.</p>