FastSAM multipoint annotation algorithm for fisheye images
The multipoint representation method has significant advantages in the field of people detection in fisheye images.However,its annotation process is time-consuming and labor-intensive.Therefore,this paper proposes a multipoint annotation algorithm based on FastSAM.First,the rectangular bounding boxes annotated on the fisheye image datasets are used as prompt boxes,which are input into FastSAM along with the original image to obtain more accurate object segmentation annotations.The accuracy of the segmentation information is evaluated according to the IoU between the segmentation annotation and the prompt box.For inaccurate annotations,further manual screening and correction are performed.To address the issue of the multipoint representation's inability to handle cases in which the center point is not inside the target,we propose a convex hull-based multipoint representation re-gression strategy.This strategy can directly obtain multipoint representation annotations through segmentation infor-mation,and a corresponding label assignment mechanism and loss function are designed.The method in this paper can save a lot of labor costs,and the feasibility of the algorithm is verified through experiments.
multipoint annotationfisheye imagepeople detectionFastSAMconvex hullprompt rectangular boxlabel assignmentloss function
万方数据
维普
