Detection Technique Tailored for Small Targets on Water Surfaces in Unmanned Vessel Scenarios

• Published in: Journal of marine science and engineering Vol. 12; no. 3; p. 379
• Main Authors: Zhang, Jian, Huang, Wenbin, Zhuang, Jiayuan, Zhang, Renran, Du, Xiang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2024
• Subjects: Accuracy; Algorithms; Artificial intelligence; Convolution; Deep learning; Design; Detection; Efficiency; Energy consumption; Feature extraction; focal modulation; Localization; Modulation; Modules; Neural networks; Object recognition; reparameterized convolution; Ship accidents & safety; ship detection; Ships; small target detection algorithm; Surface craft; Surveillance; Target detection; unmanned surface vessels; Unmanned vehicles; Vessels; Vision systems; YOLOv8; New Jersey
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse12030379

Lightweight detection methods are frequently utilized for unmanned system sensing; however, to tackle the challenge of low precision in detecting small targets on the water’s surface by unmanned surface vessels, we present an enhanced method for ship target detection tailored specifically to this context. Building upon the mainstream single-stage Yolov8 object detection model, our approach involves the integration of the Reparameterized Convolutional Spatial Oversampling Attention (RCSOSA) module, replacing the traditional Classic 2D Convolutional (C2f) module to bolster the network’s feature extraction capabilities. Additionally, we introduce a downsampling module, Spatial to Depth Convolution (SPDConv), to amplify the extraction of features relevant to small targets, thereby enhancing detection accuracy. Finally, the Focal Modulation module, based on focal modulation, replaces the SPPF (Spatial Pyramid Pooling with FPN) module, leading to a reduction in channel count, parameter volume, and an augmentation of the network’s feature representation. Experimental results demonstrate that the proposed model achieves a 3.6% increase in mAP@0.5 and a 2.1% improvement in mAP@0.5:0.95 compared to the original Yolov8 model, while maintaining real-time processing capabilities. The research validates the higher accuracy and stronger generalization capabilities of the proposed improved ship target detection method in various complex water surface environments.