SU-YOLO: Spiking Neural Network for Efficient Underwater Object Detection

• URL: http://arxiv.org/abs/2503.24389v1
• Date: Mon, 31 Mar 2025 17:59:52 GMT
• Title: SU-YOLO: Spiking Neural Network for Efficient Underwater Object Detection
• Authors: Chenyang Li, Wenxuan Liu, Guoqiang Gong, Xiaobo Ding, Xian Zhong,
• Abstract summary: Spiking Underwater YOLO (SU-YOLO) is a Spiking Neural Network (SNN) model for underwater object detection.<n>SU-YOLO incorporates a novel spike-based underwater image denoising method based solely on integer addition.<n>Results demonstrate that SU-YOLO achieves mAP of 78.8% with 6.97M parameters and an energy consumption of 2.98 mJ.
• Score: 15.935285733525962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Underwater object detection is critical for oceanic research and industrial safety inspections. However, the complex optical environment and the limited resources of underwater equipment pose significant challenges to achieving high accuracy and low power consumption. To address these issues, we propose Spiking Underwater YOLO (SU-YOLO), a Spiking Neural Network (SNN) model. Leveraging the lightweight and energy-efficient properties of SNNs, SU-YOLO incorporates a novel spike-based underwater image denoising method based solely on integer addition, which enhances the quality of feature maps with minimal computational overhead. In addition, we introduce Separated Batch Normalization (SeBN), a technique that normalizes feature maps independently across multiple time steps and is optimized for integration with residual structures to capture the temporal dynamics of SNNs more effectively. The redesigned spiking residual blocks integrate the Cross Stage Partial Network (CSPNet) with the YOLO architecture to mitigate spike degradation and enhance the model's feature extraction capabilities. Experimental results on URPC2019 underwater dataset demonstrate that SU-YOLO achieves mAP of 78.8% with 6.97M parameters and an energy consumption of 2.98 mJ, surpassing mainstream SNN models in both detection accuracy and computational efficiency. These results underscore the potential of SNNs for engineering applications. The code is available in https://github.com/lwxfight/snn-underwater.

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