Ship detection based on enhanced YOLOv3 under complex environments
NIE Xin1,2, LIU Wen1,2, WU Wei3
1. School of Navigation, Wuhan University of Technology, Wuhan Hubei 430063, China; 2. Hubei Key Laboratory of Inland Navigation Technology(Wuhan University of Technology), Wuhan Hubei 430063, China; 3. School of Information Engineering, Wuhan University of Technology, Wuhan Hubei 430070, China
Abstract：In order to improve the intelligence level of waterway traffic safety supervision, and further improve the positioning precision and detection accuracy in the ship detection algorithms based on deep learning, based on the traditional YOLOv3, an enhanced YOLOv3 algorithm for ship detection was proposed. First, the prediction box uncertain regression was introduced in the network prediction layer in order to predict the uncertainty information of bounding box. Second, the negative logarithm likelihood function and improved binary cross entropy function were used to redesign the loss function. Then, the K-means clustering algorithm was used to redesign the scales of prior anchor boxes according to the shape of ship, and prior anchor boxes were evenly distributed to the corresponding prediction scales. During training phase, the data augmentation strategy was used to expand the number of training samples. Finally, the Non-Maximum Suppression (NMS) algorithm with Gaussian soft threshold function was used to post-process the prediction boxes. The comparison experiments of various improved methods and different object detection algorithms were conducted on real maritime video surveillance dataset. Experimental results show that, compared to the traditional YOLOv3 algorithm, the YOLOv3 algorithm with prediction box uncertainty information has the number of False Positives (FP) reduced by 35.42%, and the number of True Positives (TP) increased by 1.83%, thus improving the accuracy. The mean Average Precision (mAP) of the enhanced YOLOv3 algorithm on ship images reaches 87.74%, which is improved by 24.12% and 23.53% respectively compared to those of the traditional YOLOv3 algorithm and Faster R-CNN algorithm. The proposed algorithm has the number of images detected per second reaches 30.70, meeting the requirement of real-time detection. Experimental results indicate that the proposed algorithm can achieve high-precision, robust and real-time detection of ships under adverse weather and conditions such as fog weather and low-light condition as well as the complex navigation backgrounds.
聂鑫, 刘文, 吴巍. 复杂场景下基于增强YOLOv3的船舶目标检测[J]. 计算机应用, 2020, 40(9): 2561-2570.
NIE Xin, LIU Wen, WU Wei. Ship detection based on enhanced YOLOv3 under complex environments. Journal of Computer Applications, 2020, 40(9): 2561-2570.
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2020, Vol. 40 
