  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.
