一个基于YOLOv8的循环框架,用于基于事件的对象检测
Diego A Silva1, Kamilya Smagulova1, Ahmed Elsheikh2
1Communication and Computing Systems Lab, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Frontiers in neuroscience
|February 6, 2025
概括
本研究介绍了Recurrent YOLOv8 (ReYOLOv8),通过基于事件的摄像头来增强对象检测,以在具有挑战性的条件下提高性能. ReYOLOv8提供了显著的准确性和效率,将生物视觉与人工智能结合起来,以实现强大的视觉处理.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 生物启发系统 生物启发系统
背景情况:
- 传统的RGB传感器在物体检测中与运动模糊和极端照明作斗争.
- 基于事件的摄像头在动态场景和低功耗应用中提供了卓越的性能.
- 集成基于事件的传感器与先进的物体检测对于下一代系统至关重要.
研究的目的:
- 开发一个物体检测框架,利用基于事件的摄像头.
- 通过使用事件数据进行时空建模来增强YOLOv8对象检测系统.
- 引入用于高效事件数据编码和增强的新方法.
主要方法:
- 引入了循环YOLOv8 (ReYOLOv8),将循环层集成到YOLOv8.
- 用于低延迟事件数据编码的第三级事件图像 (VTEI) 的开发量.
- 为事件传感器量身定制的实施随机极性抑制 (RPS) 数据增强.
主要成果:
- 在GEN1 (高达5%) 和PEDRo (高达18%) 数据集上,ReYOLOv8实现了显著的mAP改进.
- 减少可训练参数和模型大小,同时保持实时处理速度.
- 在具有挑战性的视觉环境中,在传统方法中表现出优越的性能.
结论:
- 基于事件的视觉传感器与ReYOLOv8等先进框架相结合,显示出显著的潜力.
- ReYOLOv8有效地将生物视觉原理与人工智能结合起来,以实现强大的视觉处理.
- 该框架可在动态和复杂的环境中实现高效可靠的对象检测.
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