River Ship Monitoring Based on Improved Deep-Sort Algorithm

As the economy develops rapidly, waterway transportation has gradually become an important part of the logistics industry. Aiming at the low detection and tracking accuracy of ship objects, a model for object detection and tracking of river ships was built. First, the dilated convolution was introduced into the backbone network of YOLOv3. A prediction scale of 104×104 and L2 regularization were introduced to heighten the network's susceptibility to small objects. A target detecting model using improved YOLOv3 was constructed. Then the improved YOLOv3 was used as the detector for the deep simple online realtime tracking algorithm's detection part. The D-IoU distance was introduced into the cascaded matching loss to build a ship tracking model based on the improved deep simple online realtime tracking algorithm. These results confirmed that the improved YOLOv3 had a detecting accuracy of 6345, a detecting time of 21.3 seconds, a recall rate of 93.25%, a missing alarm rate of 6.76%, and an average precision of 92.53%. The proposed object detection model performed the best in terms of detecting accuracy, missing and false alarm rates, and average precision indicators, with values of 87.48%, 5.14%, 12.51%, and 94.35%, respectively. The proposed ship tracking model had the highest recall rate of 64.7% and a multi-target tracking accuracy of 61.8%. This study confirms that the proposed object detection and tracking models have good performance and contribute to the intelligent development of the waterway transportation industry.