Abstract
In order to realize the real-time detection of an unmanned fishing speedboat near a ship ahead, a perception platform based on a target visual detection system was established. By controlling the depth and width of the model to analyze and compare training, it was found that the 5S model had a fast detection speed but low accuracy, which was judged to be insufficient for detecting small targets. In this regard, this study improved the YOLOv5s algorithm, in which the initial frame of the target is re-clustered by K-means at the data input end, the receptive field area is expanded at the output end, and the loss function is optimized. The results show that the precision of the improved model’s detection for ship images was 98.0%, and the recall rate was 96.2%. Mean average precision (mAP) reached 98.6%, an increase of 4.4% compared to before the improvements, which shows that the improved model can realize the detection and identification of multiple types of ships, laying the foundation for subsequent path planning and automatic obstacle avoidance of unmanned ships.
Highlights
As intelligent platforms that can be used for marine monitoring, unmanned surface ships need to complete complex and orderly autonomous operation tasks such as target recognition and obstacle avoidance when operating at high speeds on complex and uncertain surface environments
Compared to the SIFT algorithm [8] and what was proposed by David in the texture extraction algorithm [9,10,11], which are from among the representative traditional algorithms, as well as the HOG algorithm [12] proposed by the Navneet team, the deep learning target detection algorithm has made a great leap in performance and accuracy, and its model network’s anti-scale change and anti-translation capabilities have been significantly improved
There were six types of ships in the dataset used in this study, so the Mean average precision (mAP) calculation was the average of the six types of AP, the value of which was the area enclosed by the recall and precision curves, as in Equation (10): recall =
Summary
As intelligent platforms that can be used for marine monitoring, unmanned surface ships need to complete complex and orderly autonomous operation tasks such as target recognition and obstacle avoidance when operating at high speeds on complex and uncertain surface environments. Accurate recognition and automatic obstacle avoidance place high requirements on the high-speed information processing capabilities of the vision system of an unmanned ship [1]. Unmanned platforms are developing rapidly and becoming more mature Equipment such as unmanned aerial vehicles and unmanned vehicles has gradually become more widely used. The establishment of a visual inspection system for ships has become a hot issue for autonomous ships at sea. In terms of a visual inspection system for ships has become a hot issue for autonomous ships at sea
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