本文提出了一种针对YOLOV7的弱监督与自适应目标检测算法(WSA-YOLO),该算法通过自适应增强有效提升了低光环境中的目标检测能力,解决了这一实际问题。提出的分解网络将图像分解为反射图和光照图,分别进行增强。所提出的自适应残差特征块(ARFB)有效利用了低光图像和正常光图像之间的特征关联,并通过参数预测模块共享权重...
本文提出了一种 YOLOV7 弱光环境下的弱监督自适应目标检测算法 (WSA-YOLO),该算法利用自适应增强有效提高了弱光环境下的目标检测能力,解决了这一实际问题。所提出的分解网络将图像分解为反射率和照明图,然后分别进行增强。所提出的自适应残差特征块 (ARFB) 有效地利用了弱光和常光图像之间的特征相关性,并在它们...
WSA-YOLOv5s, a new algorithm with a Window Self-Attention (WSA) module is proposed in this paper to substantially improve the ability of detecting small targets while enhancing the ability of large target recognition marginally. The following fine tunings are made based on the original YOLOv5s...
WSA-YOLOv5s: improved YOLOv5s based on window self-attention module for ship detection 来自 Springer 喜欢 0 阅读量: 5 作者:ZhouWeina,WangHong,WuXintao 摘要: detection from visual image (SDVI) plays a significant role in terminal management, cross-border ship detection and marine target ...