Self-enhancement of automatic tunnel accident detection (TAD) on CCTV by AI deep-learning
This work addresses safety monitoring in tunnels by reducing false alarms, but it is incremental as it builds on an existing system with field-specific adjustments.
The paper tackled false positives in a deep-learning-based tunnel accident detection system by retraining the model with false detection data from the field, which significantly reduced false alarms for pedestrians and fire.
The deep-learning-based tunnel accident detection (TAD) system (Lee 2019) has installed a system capable of monitoring 9 CCTVs at XX site in November, 2018. The initial deep-learning training was started by studying 70,914 labeled images and label data. However, sunlight, the tail light of a vehicle, and the warning light of the working vehicle were recognized as a fire, and many pedestrians were detected in the lane of the tunnel or a black elongated black object. To solve these problems, as shown in Fig. 1, the false detection data detected in the field were trained with labeled data and reapplied in the field. As a result, false detection of pedestrians and fire could be significantly reduced.