MDCFVit-YOLO:一个用于夜间红外线小目标车辆和行人检测的模型
Huiying Zhang1, Qinghua Zhang1, Yifei Gong1
1College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, China.
PloS one
|June 17, 2025
概括
本研究介绍了MDCFVit-YOLO,这是一个用于夜间红外物体检测的先进模型. 它显著提高了小目标的准确性和回忆力,提供了增强的实时性能.
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:
- 夜间红外物体检测面临着诸如低可见度和小物体精度差等挑战.
- 现有的模型在复杂的夜间场景中扎着高干扰和精度.
研究的目的:
- 开发一个改进的对象检测模型,用于夜间红外图像.
- 为了提高红外图像中小型和低可见度目标的检测.
主要方法:
- 拟议的MDCFVit-YOLO模型将YOLOv8与Repvit骨干集成在一起.
- 引入了MPA和CSM模块,用于多尺度上下文和特征地图重量调整.
- 开发了一个动态自动检测头 (DAIH) 和新的焦点器损失功能.
主要成果:
- 对于小型红外夜间目标,实现了78%的检测准确率,58.6%的回忆率和67%的mAP.
- 与YOLOv8相比,MDCFVit-YOLO显示了6.4%的mAP增加,精度和回忆能力得到了改善.
- 该型号具有20.9M参数,平衡性能与效率.
结论:
- MDCFVit-YOLO模型在夜间红外线物体检测方面取得了重大进展.
- 拟议的模块和损失函数有效地提高了检测准确度和实时功能.
- 这项研究提供了改进红外目标检测系统的新方法.
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