A Novel Intelligent Ship Detection Method Based on Attention Mechanism Feature Enhancement

The intelligent perception ability of the close-range navigation environment is the basis of autonomous decision-making and control of unmanned ships. In order to realize real-time perception of the close-range environment of unmanned ships, an enhanced attention mechanism YOLOv4 (EA-YOLOv4) algorit...

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Bibliographic Details
Published inJournal of marine science and engineering Vol. 11; no. 3; p. 625
Main Authors Ye, Yingdong, Zhen, Rong, Shao, Zheping, Pan, Jiacai, Lin, Yubing
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2023
Subjects
Accuracy
Algorithms
Artificial intelligence
attention mechanism
Decision making
Deep learning
Detection
Detectors
Dimensions
Efficiency
Intersections
Methods
Navigation
Neural networks
Object recognition
Perception
Real time
Semantics
Shipping industry
Ships
target detection
Teaching methods
Temporal perception
unmanned ship
Unmanned vehicles
water transportation
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Summary:The intelligent perception ability of the close-range navigation environment is the basis of autonomous decision-making and control of unmanned ships. In order to realize real-time perception of the close-range environment of unmanned ships, an enhanced attention mechanism YOLOv4 (EA-YOLOv4) algorithm is proposed. First of all, on the basis of YOLOv4, the convolutional block attention module (CBAM) is used to search for features in channel and space dimensions, respectively, to improve the model’s feature perception of ship targets. Then, the improved-efficient intersection over union (EIoU) loss function is used to replace the complete intersection over union (CIoU) loss function of the YOLOv4 algorithm to improve the algorithm’s perception of ships of different sizes. Finally, in the post-processing of algorithm prediction, soft non-maximum suppression (Soft-NMS) is used to replace the non-maximum suppression (NMS) of YOLOv4 to reduce the missed detection of overlapping ships without affecting the efficiency. The proposed method is verified on the large data set SeaShips, and the average accuracy rate of mAP0.5–0.95 reaches 72.5%, which is 10.7% higher than the original network YOLOv4, and the FPS is 38 frames/s, which effectively improves the ship detection accuracy while ensuring real-time performance.
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse11030625