CKAN-YOLOv8: A Lightweight Multi-Task Network for Underwater Target Detection and Segmentation in Side-Scan Sonar
Underwater target detection and segmentation in Side-Scan Sonar (SSS) imagery is challenged by low signal-to-noise ratios, geometric distortions, and Unmanned Underwater Vehicles (UUVs)’ computational constraints. This paper proposes CKAN-YOLOv8, a lightweight multi-task network integrating Kolmogor...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/13/5/936 |
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| Summary: | Underwater target detection and segmentation in Side-Scan Sonar (SSS) imagery is challenged by low signal-to-noise ratios, geometric distortions, and Unmanned Underwater Vehicles (UUVs)’ computational constraints. This paper proposes CKAN-YOLOv8, a lightweight multi-task network integrating Kolmogorov–Arnold Networks Convolution (KANConv) into YOLOv8. The core innovation replaces conventional convolutions with KANConv blocks using learnable B-spline activations, dynamically adapting to noise and multi-scale targets while ensuring parameter efficiency. The KANConv-based Path Aggregation Network (KANConv-PANet) mitigates geometric distortions through spline-optimized multi-scale fusion. A dual-task head combines CIoU loss-driven detection and a boundary-sensitive segmentation module with Dice loss. Evaluated on a dataset (50 raw images augmented to 2000), CKAN-YOLOv8 achieves state-of-the-art performance as follows: 0.869 AP@0.5 and 0.72 IoU, alongside real-time inference at 66 FPS. Ablation studies confirm the contributions of KANConv modules to noise robustness and multi-scale adaptability. The framework demonstrates exceptional robustness to noise, scalability across target sizes. |
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| ISSN: | 2077-1312 |