软件导刊2024，Vol.23Issue(1)：128-134,7.DOI:10.11907/rjdk.222521
一种圆形锚框的Faster R-CNN小目标检测算法
A Faster R-CNN Small Object Detection Algorithm Based on Circular Anchor
摘要
Abstract
The main task of small object detection is to detect images with dimensions smaller than 32×32 pixel target and classify it.Due to the inaccurate matching of traditional rectangular anchor frame structures in detecting small targets,the number of small targets in the general dataset is small and their distribution is uneven,which will lead to poor model detection performance.Therefore,based on Faster R-CNN,a small target detection method with circular anchor frames is proposed.In the RPN stage,a circular anchor frame is used to locate the region of interest,and a new area intersection and union ratio calculation method and loss function are used to reduce the model parameter quantity and offset calculation in the anchor frame regression stage,in order to enhance the model's fitting ability to the detected target and improve the model's detection accuracy and efficiency.At the same time,in order to address the issues of low proportion and uneven distribution of small targets in existing public datasets,data augmentation was performed on the MS COCO 2017 dataset,retaining only the small targets and modi-fying the annotation information to a circular bounding box with a high wrapping rate for the small targets.Experiments have shown that the cir-cular anchor box method and data augmentation method have better detection performance in detecting small targets,with detection efficiency and speed significantly better than Faster R-CNN,APS and detection speed have been improved by 4.1%and 4 FPS,respectively.关键词
小目标检测/Faster R-CNN/圆形锚框/数据增强/圆交并比Key words
small target detection/Faster R-CNN/circular anchor/data augmentation/circle intersection over union分类
计算机与自动化引用本文复制引用
闫春相,徐遵义,刘康宁,李晨..一种圆形锚框的Faster R-CNN小目标检测算法[J].软件导刊,2024,23(1):128-134,7.基金项目
国家自然科学基金青年基金项目(62102235) （62102235）
山东省重点研发计划(重大科技创新工程)项目(2021CXGC011204) （重大科技创新工程）
