GDEIM-SF:一个轻量级的无人机检测框架,将脱雾和低光增强相结合
1College of Urban Construction, Yangtze University, Jingzhou 434100, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本研究介绍了一种空中视觉框架,用于在雾和弱光等具有挑战性的条件下强大的车辆和行人检测. 该方法提高了图像质量,并采用轻量级检测架构,提高了无人机应用的准确性和效率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 图像退化 (雾,低照明,遮蔽) 在复杂的交通中妨碍了车辆和行人检测.
- 可靠的检测对于无人机 (UAV) 视觉感知任务至关重要.
研究的目的:
- 开发一个空中视觉框架,将多层次的图像增强与轻量级检测架构集成在一起.
- 在不利的成像条件下提高对象检测的可靠性和效率.
主要方法:
- 一个级联的"dehazing + enhancement"模块预处理图像,恢复细节并提高低光区域的结构保真度.
- 一个轻量级的检测架构,GDEIM-SF,结合了GoldYOLO骨干与D-FINE无解码器.
- 整合了CAPR和ASF模块,用于增强边缘建模和多尺度语义对齐.
主要成果:
- 与类似的轻量级模型相比,拟议的方法在VisDrone数据集上的mAP@50-90实现了2.5-2.7个百分点的改进.
- 保持了较低的参数数量和计算开销,证明了效率.
- 在检测准确性,推断速度和部署适应性之间实现了平衡的权衡.
结论:
- 空中视觉框架为在具有挑战性的环境中基于无人机的视觉感知提供了实用和高效的解决方案.
- 综合方法有效地解决了图像退化问题,以改善对象检测.
- 轻量级但坚固的设计使部署适应现实世界的应用程序更容易.
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