Abstract: Object counting aims to obtain the number of specific types of objects such as vehicles,buildings,people contained in a given image,which is of great significance to urban planning,emergency response,national security,etc.The current object coun-ting task mainly relies on the images taken by low-altitude cameras,and there are obvious problems such as the object being easily occluded and the small counting space range.Widespread use of high-definition aerial remote sensing imagery makes it possible to count objects in large areas.However,the object counting task for aerial images has challenges such as large differences in object scales,dense distribution,and uncertain orientation.Existing object detection counting models and regression counting models based on low-altitude images are not suitable for object counting in aerial images.To solve this problem,this paper proposes an adaptive object counting model for aerial images.Firstly,the geometric adaptive Gaussian convolution method is used to solve the problem of object scale variation.Then,the image loss judgment method based on structural similarity is used to solve the pro-blem of poor counting stability of object dense regions.Experimental analysis shows that the proposed model can achieve better object count accuracy than the benchmark model.

Key words: Object counting, Aerial imagery, Regression counting, Gaussian convolution, Structural similarity