Red-light Running increases the risk of collisions and traffic accidents. When a car runs a red light, it can cause a collision with other vehicles moving along the main road, causing serious accidents and even leading to casualties. In Vietnam, many traffic accidents are caused by red-light running. This research paper presents a novel approach for detecting red-light running violations for Vietnamese intersections by leveraging object detection techniques and the YOLO (You Only Look Once) algorithm, a deep neural learning model that uses convolutional neural network architecture (CNNs) for object detection in real-time. The proposed system utilizes CCTV video footage to capture video frames, which are then processed through a trained YOLOv8 model to identify red-light violators. The system’s performance is evaluated based on detection accuracy and processing speed and validated against a custom build dataset extracted from CCTV footages of Vietnamese streets. The experimental results demonstrate high accuracy and processing efficiency up to 93.4% mAP50, 89.2% precision and 92.6% recall, indicating that the proposed approach is suitable for deployment in the context of Vietnamese traffic conditions. The proposed system has significant potential to enhance road safety and mitigate the incidence of red-light running violations in Vietnam.