Port Ship Detection in Complex Environments
Xinqiang Chen, Lei Qi, Yongsheng Yang, Octavian Adrian Postolache, Zewei Yu, Xueqian Xu · 2019
Ship detection plays an important role in the port monitor and management. The difficulty in ship detection is that with the change of ship traffic flow, limited with small ship imaging scales and face the foggy navigation. The traditional ship image detection method has insufficient detection performance, which cannot meet the needs of the port. In order to improve the performance of ship detection in the port, we detect ship by yolov3 detection algorithm to solve the above problems. This ship detection framework based on convolutional neural networks. The characteristics of the ship are extracted layer by layer through the DarkNet53 network, and multi-scale image pyramid features are formed to detect ships of different scales. Specifically, large ships, medium ships, small ships. And uses yolov3 algorithm to achieve ship detection. We have selected four typical port navigation scenes: (1) small traffic flow (2)foggy ship navigation (3)large traffic flow (4) small imaging scale. Experiments show that the ship detection based on yolov3 has high accuracy in the face of complex sea navigation conditions and can cope with the detection requirements of different scenes at sea.