摘要
针对交通路口图像复杂,小目标难测且目标之间易遮挡以及天气和光照变化引发的颜色失真、噪声和模糊等问题,提出一种基于YOLOv9(You Only Look Once version 9)的交通路口图像的多目标检测算法ITD-YOLOv9(Intersection Target Detection-YOLOv9)。首先,设计CoT-CAFRNet(Chain-of-Thought prompted Content-Aware Feature Reassembly Network)图像增强网络,以提升图像质量,并优化输入特征;其次,加入通道自适应特征融合(iCAFF)模块,以增强小目标及重叠遮挡目标的提取能力;再次,提出特征融合金字塔结构BiHS-FPN(Bi-directional High-level Screening Feature Pyramid Network),以增强多尺度特征的融合能力;最后,设计IF-MPDIoU(Inner-Focaler-Minimum Point Distance based Intersection over Union)损失函数,以通过调整变量因子,聚焦关键样本,并增强泛化能力。实验结果表明,在自制数据集和SODA10M数据集上,ITD-YOLOv9算法的检测精度分别为83.8%和56.3%,检测帧率分别为64.8 frame/s和57.4 frame/s。与YOLOv9算法相比,ITD-YOLOv9算法的检测精度分别提升了3.9和2.7个百分点。可见,所提算法有效实现了交通路口的多目标检测。
Aiming at the problem of complex traffic intersection images,the difficulty in detecting small targets,and the tendency for occlusion between targets,as well as the color distortion,noise,and blurring caused by changes in weather and lighting,a multi-target detection algorithm ITD-YOLOv9(Intersection Target Detection-YOLOv9)for traffic intersection images based on YOLOv9(You Only Look Once version 9)was proposed.Firstly,the CoT-CAFRNet(Chain-of-Thought prompted Content-Aware Feature Reassembly Network)image enhancement network was designed to improve image quality and optimize input features.Secondly,the iterative Channel Adaptive Feature Fusion(iCAFF)module was added to enhance feature extraction for small targets as well as overlapped and occluded targets.Thirdly,the feature fusion pyramid structure BiHS-FPN(Bi-directional High-level Screening Feature Pyramid Network)was proposed to enhance multi-scale feature fusion capability.Finally,the IF-MPDIoU(Inner-Focaler-Minimum Point Distance based Intersection over Union)loss function was designed to focus on key samples and enhance generalization ability by adjusting variable factors.Experimental results show that on the self-made dataset and SODA10M dataset,ITD-YOLOv9 algorithm achieves 83.8%and 56.3%detection accuracies and 64.8 frame/s and 57.4 frame/s detection speeds,respectively;compared with YOLOv9 algorithm,the detection accuracies are improved by 3.9 and 2.7 percentage points respectively.It can be seen that the proposed algorithm realizes multi-target detection at traffic intersections effectively.
作者
廖炎华
鄢元霞
潘文林
LIAO Yanhua;YAN Yuanxia;PAN Wenlin(School of Electrical and Information Engineering,Yunnan Minzu University,Kunming Yunnan 650504,China;Yunnan Key Laboratory of Unmanned Autonomous Systems(Yunnan Minzu University),Kunming Yunnan 650504,China;Chengdu Xinjin Electric Power Supply Branch Company,State Grid Sichuan Electric Power Company,Xinjin Sichuan 611430,China;School of Mathematics and Computer Science,Yunnan Minzu University,Kunming Yunnan 650504,China)
出处
《计算机应用》
北大核心
2025年第8期2555-2565,共11页
journal of Computer Applications
基金
国家自然科学基金资助项目(62362071)。
关键词
YOLOv9
交通路口检测
自适应融合
多目标检测
深度学习
YOLOv9(You Only Look Once version 9)
traffic intersection detection
adaptive fusion
multi-target detection
deep learning
