A Population Statistics Method of Dense Small Targets Indoor
The number counting of dense small targets in computer vision tasks is socially important in indoor scenari-os such as crowd behavior analysis,optimal resource alloca-tion,and modern security.Existing dense small target counting methods have problems such as omission caused by mutual occlusion of targets,misdetection due to dense de-tection of targets,and small targets and insufficient extrac-tion of face features.Aiming at the problems of omission,misdetection and insufficient features of dense small targets in indoor scenes,we propose a statistical model STO-YOLO based on the YOLOv5 framework,which firstly adds a detection module for dense small targets to the backbone network of YOLOv5 to improve the feature extraction ca-pability,then adds a small target detection module to the Neck network to enhance the feature extraction capabili-ty,and then adds a small target detection module to the Neck network to improve the feature extraction capabili-ty.The method firstly adds a small target detection mod-ule to the backbone network of YOLOv5 to improve the feature extraction capability,and then adds a small tar-get detection module to the feature fusion network to en-hance the feature fusion capability,so as to improve the misdetection problem of dense small targets far away from the surveillance;secondly,it introduces the OTA mechanism,which treats the label assignment as the op-timal transmission problem,and at the same time com-bines with the contextual information to reduce the num-ber of fuzzy frames to reduce the error generated by the target obstruction.Self-constructed dataset and validate the proposed method in a real teaching scenario.The ex-perimental results show that compared with the SOTA method YOLOv5,the precision and recall indexes of STO-YOLO detection results are significantly improved;compared with the latest YOLOv8,the recall and mAP indexes are also improved,which fully verifies the pro-posed STO-YOLO method.
NETL
NSTL
万方数据
维普
