基于YOLO-v5算法的航拍图像小目标检测改进算法  

Improved algorithm for small target detection in aerial images based on YOLO-v5

作　　者：郝紫霄 王琦 高尚 HAO Zixiao;WANG Qi;GAO Shang(School of Computer,Jiangsu University of Science and Technology,Zhenjiang 212100,China)

机构地区：[1]江苏科技大学计算机学院,江苏镇江212100

出　　处：《常州大学学报（自然科学版）》2023年第6期45-51,共7页Journal of Changzhou University：Natural Science Edition

基　　金：江苏省高等学校基础科学(自然科学)研究面上资助项目(21KJB510028)。

摘　　要：航拍图像具有数据量大、目标尺度小而分布稠密的特征,且其视角是俯视,不同于普通图像的平视,因此针对普通图像的传统目标检测算法无法适应航拍图像的目标检测任务。针对航拍图像小目标检测,提出了一种基于YOLO-v5的改进算法Small-Tiny-YOLO-v5。首先,按照GhostNet网络结构搭建改进算法的骨干网络;其次,在骨干网络内部加入注意力机制模块;再次,构建了一个针对微小目标的航拍图像数据集。此外,在改进算法训练阶段融合了迁移学习的思想。实验结果表明,所提改进算法的模型参数量远低于原始YOLO算法;精度与速度也优于原始算法,在公开数据集与本文构建的数据集中,精度分别提升了0.009和0.024,速度分别提升了73.735%和58.641%。Aerial image has the characteristics of large amount of data,small target scale and dense distribution,and its angle of view is downcast,which is different from the head up view of ordinary image.Therefore,the traditional target detection algorithm for ordinary image cannot adapt to the target detection task of aerial image.For small target detection in aerial images,an improved algorithm based on YOLO-v5,Small-Tiny-YOLO-v5,was proposed.Firstly,GhostNet network was used as the backbone network of the improved algorithm;secondly,the attention mechanism module was added in the backbone network;thirdly,an aerial image data set for small targets was constructed.In addition,the idea of transfer learning was integrated in the training of the improved algorithm.Experimental results show that the model parameters of the proposed improved algorithm are lower than the original YOLO algorithm, and the accuracy and speed are also better than the original algorithm. In the public dataset and the dataset constructed in this paper, the accuracy has increased by 0.009 and 0.024 respectively, and the speed has increased by 73.735% and 58.641% respectively.

关 键 词：航拍图像 小目标检测 轻量骨干网络 注意力机制 

分 类 号：TP75[自动化与计算机技术—检测技术与自动化装置]