Lin Yu-Wei, Chiu Chu-Fu, Chen Li-Hsien, Ho Chao-Ching
Department of Mechanical Engineering, National Taipei University of Technology, Taipei City 10608, Taiwan.
Department of Civil Engineering, National Taipei University of Technology, Taipei City 106344, Taiwan.
J Imaging. 2024 Mar 26;10(4):78. doi: 10.3390/jimaging10040078.
Taiwan, frequently affected by extreme weather causing phenomena such as earthquakes and typhoons, faces a high incidence of rockfall disasters due to its largely mountainous terrain. These disasters have led to numerous casualties, government compensation cases, and significant transportation safety impacts. According to the National Science and Technology Center for Disaster Reduction records from 2010 to 2022, 421 out of 866 soil and rock disasters occurred in eastern Taiwan, causing traffic disruptions due to rockfalls. Since traditional sensors of disaster detectors only record changes after a rockfall, there is no system in place to detect rockfalls as they occur. To combat this, a rockfall detection and tracking system using deep learning and image processing technology was developed. This system includes a real-time image tracking and recognition system that integrates YOLO and image processing technology. It was trained on a self-collected dataset of 2490 high-resolution RGB images. The system's performance was evaluated on 30 videos featuring various rockfall scenarios. It achieved a mean Average Precision (mAP50) of 0.845 and mAP50-95 of 0.41, with a processing time of 125 ms. Tested on advanced hardware, the system proves effective in quickly tracking and identifying hazardous rockfalls, offering a significant advancement in disaster management and prevention.
台湾地区经常受到地震和台风等极端天气影响，因其多山地形，落石灾害发生率很高。这些灾害导致众多人员伤亡、政府赔偿案例，并对交通安全产生重大影响。根据国家灾害防救科技中心2010年至2022年的记录，866起土石灾害中有421起发生在台湾东部，落石导致交通中断。由于传统灾害探测器的传感器仅在落石发生后记录变化，目前没有能够在落石发生时进行探测的系统。为解决这一问题，开发了一种利用深度学习和图像处理技术的落石探测与跟踪系统。该系统包括一个集成了YOLO和图像处理技术的实时图像跟踪与识别系统。它在一个由2490张高分辨率RGB图像组成的自采集数据集上进行了训练。该系统在30个具有各种落石场景的视频上进行了性能评估。它的平均精度均值（mAP50）为0.845，mAP50 - 95为0.41，处理时间为125毫秒。在先进硬件上进行测试后，该系统被证明能够有效快速地跟踪和识别危险落石，在灾害管理和预防方面取得了重大进展。
