【摘要】 针对航空图像小目标检测存在的检测精度低、误检与漏检严重等问题，提出一种基于改进Yolov5l的航空小目标检测算法（AS-Yolov5）。在Yolov5的主干特征提取网络中引入空洞卷积，使用Transform的Decode模块，在特征融合网络中新增检测头，FPN+PAN特征融合时设置融合权重，输出端采用SE-Net注意力机制，测试时进行多尺寸输入及测试时间增强（TTA）。算法在visdron2021数据集上进行验证，实验结果表明，AS-Yolov5的均值平均精度@0.5 (mAP@0.5)为41.0%，较Yolov5l的28.5%提升12.5%，有效提高Yolov5l难以在远距离、暗环境、密集分布和图像模糊的场景下的小目标检测能力。更多还原

【Abstract】 For the detection of small targets in aerial images,there are problems such as low detection accuracy,false detection and serious missed detection,an aviation small target detection algorithm was proposed(AS-Yolov5)based on improved Yolov5l.In the backbone,the atrous spatial pyramid pooling model was inserted,so that the receptive field was expanded without increasing the number of layers.Transformer encoding block was inserted to obtain layer global information.In the neck,a new detection head was added to improve ability of face dense and complex targets.Fusion weights were added to focus on the relationship between different layers and small targets.The detection head used SE-Net attention mechanism,by selflearning the channel information,the learning ability of small targets was improved.Multi-size input and test time enhancement(TTA)were performed during testing to improve model recognition capabilities.The proposed detection method was experimentally verified on the visdron2021data set.Experimental results show that the mAP@0.5of AS-Yolov5is 41.0%,which is12.5% higher than that of Yolov5l.This method effectively improves the detection ability of aviation small targets.更多还原