改进YOLOv5的无人机影像道路目标检测算法OA北大核心
Road target detection algorithm based on improved YOLOv5 in UAV images
针对无人机影像中道路小目标漏检和目标之间遮挡导致的目标检测精度低、 鲁棒性差等问题,提出一种多尺度融合卷积注意力模块(Convolutional block attention module,CBAM)的YOLOv5道路目标检测算法,即YOLOv5s-FCC.首先,引入小目标感知层对模型进行多尺度改进,增加一个针对小目标的YOLO检测头以提高网络对道路中小目标的特征提取能力.其次,利用CBAM融合空间和通道信息以增强网络中的重要信息,通过将CBA…查看全部>>
Aiming at the problems such as low accuracy and poor robustness of target detection caused by missed detection of small road targets and occlusion between targets in UAV images, an improved road target detection algorithm based on YOLOv5 combining convolutional block attention module(CBAM), called YOLOv5s-FCC, was proposed. Firstly, a small target sensing layer was introduced to improve the multi-scale model, and a small target YOLO detection head was added …查看全部>>
张翼;马荣贵;梁辰
长安大学信息工程学院,陕西西安 710021长安大学信息工程学院,陕西西安 710021长安大学信息工程学院,陕西西安 710021
无人机道路目标检测YOLOv5损失函数卷积注意力模块
unmanned aerial vehicle(UAV)road target detectionYOLOv5loss functionconvolutional block attention module (CBAM)
《测试科学与仪器》 2024 (1)
128-139,12
This work was supported by Key Research and Development Project of China(No.2021YFB1600104)National Natural Science Foundation of China(No.52002031)Scientific Research Project of Shaanxi Provincial Department of Transportation(No.20-24K,20-25X)
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