Application of Improved YOLO V5s Model for Regional Poverty Assessment using Remote Sensing Image Target Detection
This study aims at applying the improved YOLO V5s model for the assessment of regional poverty using remote sensing image target detection.For this purpose,three improvements were made to the model.So,a new en-hanced PAN structure was proposed.Accordingly,a new RIOU_Loss regression loss function of bounding box was pro-posed.Furthermore,a new collaborative attention mechanism was put forward.In addition,while objects in the remote sensing images were used as the representations of poverty status,the changes in the images were considered to evalu-ate the regional poverty rate in a continuous time interval.The results show that the values of P,R,mAP@0.5 and mAP@0.5:0.95 of the model are improved,while the Loss value is decreased.Therefore,compared with the original model,the improved model has more accurate object detection capabilities.Meanwhile,compared with traditional sta-tistical data methods,the improved model provides an equivalent dataless evaluation approach for regional poverty as-sessment.
NETL
NSTL
万方数据
