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We propose AI based harbor surveillance system consisting of object recognition and anomaly situation determination. In the object recognition, YOLOv5, which had the best performance with an mAP of 0.953, was used. The abnormal situation was determined through the distance between the recognized objects. The comparison of the determination results from real and estimated distance are same.
and We have several limitation for this research.
First limitation is lower recognition rate for small object such as person and bike. It is because that, YOLO model determines
objects by dividing a fixed cell grid to match realtime performance. In order to solve the problem, we will research the object detection model for considering small size target scenario.
Moreover, there is another limitation in the early detection of anomaly situations. We set the criteria for anomaly situations based on only distance that is less than 20 meters. According to the simple criteria, in the experimental results, only 1.5 seconds was given from anomaly detection to collision. In future research, it is necessary to classify the warning stage so that anomalies can be recognized even at a longer distance based on the direction and speed from AIS information.
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IEEE/ICACT20230138 Slide.12
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