Lightweight detection methods are frequently utilized for unmanned system sensing; however, when put in complicated water surface environments, they suffer from insufficient feature fusion and decreased accuracy. This paper proposes a lightweight surface target detection algorithm with multi-scale feature fusion augmentation in an effort to improve the poor detection accuracy of lightweight detection algorithms in the mission environment of unmanned surface vehicles (USVs). Based on the popular one-stage lightweight YOLOv7-Tiny target detection algorithms, a lightweight extraction module is designed first by introducing the multi-scale residual module to reduce the number of parameters and computational complexity while improving accuracy. The Mish and SiLU activation functions are used to enhance network feature extraction. Second, the path aggregation network employs coordinate convolution to strengthen spatial information perception. Finally, the dynamic head, which is based on the attention mechanism, improves the representation ability of object detection heads without any computational overhead. According to the experimental findings, the proposed model has 22.1% fewer parameters than the original model, 15% fewer GFLOPs, a 6.2% improvement in mAP@0.5, a 4.3% rise in mAP@0.5:0.95, and satisfies the real-time criteria. According to the research, the suggested lightweight water surface detection approach includes a lighter model, a simpler computational architecture, more accuracy, and a wide range of generalizability. It performs better in a variety of difficult water surface circumstances.

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.

In response to reducing the energy cost of unmanned surface vehicles (USVs) while overcoming the low accuracy problem in surface target detection, a lightweight detection algorithm with multi-scale feature fusion is proposed. Based on the popular one-stage lightweight Yolov7-tiny target detection model, a lightweight extraction module is designed first by introducing the multiscale residual module to reduce the number of parameters and computational complexity while improving accuracy. The Mish and SiLU activation functions are used to enhance network feature extraction. Second, the path aggregation network employs coordinate convolution to strengthen spatial information perception. Finally, the dynamic head, which is based on the at-tention mechanism, improves the representation ability of object detection heads without any computational overhead. According to the experimental findings, the proposed model has 22.1% fewer parameters than the original model, 15% fewer GFLOPs, a 6.2% improvement in mAP@0.5, a 4.3% rise in mAP@0.5:0.95, and it satisfies the real-time criteria. According to the research, the suggested lightweight water surface detection approach includes a lighter model, a simpler computational architecture, more accuracy, and a wide range of generalizability. It performs bet-ter in a variety of difficult water surface circumstances.

Infrared and visible image fusion is a very effective way to solve the degradation of sea images for unmanned surface vessels (USVs). Fused images with more clarity and information are useful for the visual system of USVs, especially in harsh marine environments. In this work, three novel fusion strategies based on adaptive weight, cross bilateral filtering, and guided filtering are proposed to fuse the feature maps that are extracted from source images. First, the infrared and visible cameras equipped on the USV are calibrated using a self-designed calibration board. Then, pairs of images containing water scenes are aligned and used as experimental data. Finally, each proposed strategy is inserted into the neural network as a fusion layer to verify the improvements in quality of water surface images. Compared to existing methods, the proposed method based on adaptive weight provides a higher spatial resolution and, in most cases, less spectral distortion. The experimental results show that the visual quality of fused images obtained based on an adaptive weight strategy is superior compared to other strategies, while also providing an acceptable computational load.

The radar-photoelectric system is a perception system to detect the surrounding environment based on marine radar and a photoelectric device. Mast obscuration, green water, and multi-object scenes are special scenes that appear in the first-frame image during the navigation of unmanned surface vehicles. The perception system cannot accurately obtain the object information in mast obscuration and green water scenes. The radar-guided object cannot be stably extracted from the first-frame image in multi-object scenes. Therefore, this paper proposes an object extraction algorithm for the first-frame image of unmanned surface vehicles based on a radar-photoelectric system. The algorithm realizes the field-of-view adaptation to solve the problem that the features of the radar-guided object are incomplete in the first-frame image and improve the detection accuracy of the local features by 16.8%. The algorithm realizes the scene recognition of the first-frame image to improve the robustness of object tracking. In addition, the algorithm achieves the stable extraction of the radar-guided object in multi-object scenes.