YOLO-Point: Effective Key Points Detection for Optimal Break Points on Vehicle Windows

The optimal break points detection of vehicle windows is of great significance for various safety and emergency response scenarios, such as unmanned rescue operations and accidental vehicle lock-ins. Identifying the best locations to break a vehicle window can significantly reduce the time and effort required to gain entry, while minimizing potential injury or damage to the vehicle’s occupants. Unfortunately, there is currently a lack of comprehensive datasets specifically focused on the optimal break points of vehicle windows. This absence of available data hampers the advancement of research and development in this area. Given this, this study establishes a novel dataset, named "VeBP", dedicated to the optimal break points on vehicle windows. This dataset provides comprehensive images and labels that can be used by researchers and engineers to develop and test new methodologies and technologies for efficient window breakage. Additionally, inspired by the design principle of YOLO, an effective key points detection model named "YOLO-Point" is proposed for accurately identifying the optimal break points on vehicle windows. Extensive experiments were conducted to validate the effectiveness and performance of the proposed model on the newly established dataset. The results demonstrate that the proposed model not only achieves high accuracy but also operates efficiently, making it a practical solution for real-world applications in emergency scenarios and beyond.