通过自适应视觉转换器进行节能和对抗性弹性水下物体检测
Leqi Li1, Gengpei Zhang1, Yongqian Zhou1
1The School of Electronic Information and Electrical Engineering, East Campus, Yangtze University, Jingzhou 434100, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
本研究引入了适应视觉转换器 (A-ViT) 框架,用于强大的水下物体检测,显著提高图像质量和检测准确性,同时减少延迟并增强对敌对攻击的安全性.
科学领域:
- 计算机视觉 计算机视觉
- 海洋技术 海洋技术
- 人工智能的人工智能
背景情况:
- 水下物体检测面临着诸如光学退化,高能耗和对抗威胁等挑战.
- 现有的方法在不同的海洋环境中与图像质量和计算效率作斗争.
研究的目的:
- 开发一个基于自适应视觉转换器 (A-ViT) 的框架,用于增强水下物体检测.
- 提高海上应用的图像质量,检测精度和系统效率.
- 提高系统对抗对抗干扰的弹性,并确保运营可行性.
主要方法:
- 实施了用于硬件可行性的功率建模和耐久性估计方案.
- 使用混合注意力变压器 (HAT) 进行超分辨率和DICAM进行分阶段的图像增强.
- 采用了改进的YOLOv11-Coordinate Attention-High-order空间特征金字塔网络 (YOLOv11-CA_HSFPN) 来进行检测.
- 整合了适应视觉变压器 (A-ViT) 与感兴趣区域 (ROI) 聚合以提高效率.
- 引入了一个图像阶段攻击快速检查 (IAQ) 模块,用于防御对手攻击.
主要成果:
- 在图像质量指标方面取得了显著改善:PSNR (+74.8%),SSIM (+375.8%),UIQM (3.00至3.85) 和UCIQE (0.550至0.673).
- YOLOv11-CA_HSFPN达到了56.2%的mAP@0.5,超过了基线YOLOv11的1.5%.
- 与YOLOv11-CA_HSFPN一起,A-ViT + ROI可将推理延迟降低27.3%,内存使用率降低74.6%.
- 与其他探测器相比,显示了高达48.9%的延迟减少和80.0%的VRAM节省.
- 该IAQ模块减少了33-40%的对抗攻击诱导的延迟增长.
结论:
- 拟议的A-ViT框架有效地解决了水下物体检测的关键挑战.
- 该系统在图像增强,检测精度和计算效率方面表现出卓越的性能.
- 该框架提供了针对敌对攻击的强有力的防御机制,确保在关键的海上任务中可靠运行.
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