A Faster R-CNN Small Object Detection Algorithm Based on Circular Anchor
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.
small target detectionFaster R-CNNcircular anchordata augmentationcircle intersection over union
国家科技期刊平台
NSTL
万方数据
维普
