Ship Target Detection Based on Improved YOLO Network

Hong Huang,Dechao Sun,Bangquan Liu,Chun Zhu,Renfang Wang

doi:10.1155/2020/6402149

Hong Huang, Dechao Sun + Show 3 more

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https://doi.org/10.1155/2020/6402149

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Abstract

Ship target detection is an important guarantee for the safe passage of ships on the river. However, the ship image in the river is difficult to recognize due to the factors such as clouds, buildings on the bank, and small volume. In order to improve the accuracy of ship target detection and the robustness of the system, we improve YOLOv3 network and present a new method, called Ship-YOLOv3. Firstly, we preprocess the inputting image through guided filtering and gray enhancement. Secondly, we use k-means++ clustering on the dimensions of bounding boxes to get good priors for our model. Then, we change the YOLOv3 network structure by reducing part of convolution operation and adding the jump join mechanism to decrease feature redundancy. Finally, we load the weight of PASCAL VOC dataset into the model and train it on the ship dataset. The experiment shows that the proposed method can accelerate the convergence speed of the network, compared with the existing YOLO algorithm. On the premise of ensuring real-time performance, the precision of ship identification is improved by 12.5%, and the recall rate is increased by 11.5%.

Highlights

With the vigorous development of shipping industry, the water traffic is more and more busy
We load the weight of PASCAL VOC dataset into the model and train it on the ship dataset. e experiment shows that the proposed method can accelerate the convergence speed of the network, compared with the existing YOLO algorithm
The YOLOv3 algorithm has a good detection effect on public dataset, the ship dataset used in this paper is obtained by monitoring video. e ship image is fuzzy at night or in foggy weather, and the gray level is uneven. e classification and detection of ships will be disturbed by these conditions. erefore, it is necessary to improve the YOLOv3 algorithm to meet the requirements of ship classification and detection in complex scenes. e overall structure of the algorithm is shown in Figure 2, which mainly includes three modules: target box dimension

Summary

Introduction

With the vigorous development of shipping industry, the water traffic is more and more busy. Due to the frequent occurrence of collisions and other accidents between ships, it is necessary to detect the types of ships effectively to ensure the safety of water traffic. E traditional methods of ship detection are based on the automatic identification system and ship features [1, 2]. Zang et al carried out ship target detection from the nonfixed platform [7, 8]. These studies have achieved good results, there are generally problems such as low recognition accuracy and human intervention. The traditional ship detection method is difficult to achieve the ideal detection effect

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