通过改进的CycleGAN数据增强和AS-YOLO框架在复杂环境中增强对象检测算法
Zhen Li1, Yuxuan Wang1, Lingzhong Meng2
1Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
Journal of imaging
|December 24, 2025
概括
本研究介绍了AS-YOLO,一种增强的对象检测框架,以及改进的CycleGAN数据增强,以应对复杂的环境. 综合方法在具有挑战性的条件下显著提高了检测准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 对象检测算法在复杂的环境中苦苦扎,如照明不佳,恶劣天气和遮蔽.
- 现有的方法需要改进,以便在现实场景中获得稳健的性能.
研究的目的:
- 开发一个增强的物体检测框架 (AS-YOLO) 和数据增强技术,以提高复杂环境中的性能.
- 解决当前在具有挑战性的条件下对象检测模型的局限性.
主要方法:
- 使用了一种改进的CycleGAN,具有双重自我注意和光谱正常化,用于数据增强.
- 开发了AS-YOLO框架,包括通道空间平行注意力,AFPN结构和Inner_IoU损失函数.
主要成果:
- 与YOLOv8n.相比,AS-YOLO显示了mAP@0.5的1.5%和mAP@0.95的0.6%的增加.
- 数据增强与风格转移进一步改善了mAP@0.5的14.6%和mAP@0.95的17.8%.
结论:
- 拟议的AS-YOLO框架和基于CycleGAN的数据增强有效地提高了复杂环境中的对象检测.
- 综合方法显示了显著的绩效增长,为具有挑战性的场景提供了强大的解决方案.
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