Automatic Detection of Small- and Medium-Sized Targets in High-Resolution Images Based on Computer Vision and Deep Learning Energy

Abstract

In order to solve the problem that it is difficult for traditional manual feature algorithms to deal with complex image features quickly and automatically in high-resolution images, an automatic detection method for small objects in high-resolution images based on computer vision and deep learning energy is proposed. Starting from the deep learning target detection system, this paper studies many problems in the system according to the characteristics of target spatial deformation, more small targets, easy confusion, and rough regional proposal in high-score remote sensing images. The results showed that a combination of spatial deformation strength with spatial deformation resistance and multiphase coupling were all proposed to be comparable with spatial deformation. The process of tracking international products in the system has been extended to study local channels. Pay attention to the operation of the machine, make full use of rich semantic data of local characteristics and the world of spatial points, which will help to improve the accuracy of the purpose of the chaw, and identify ways to create a shared database. The proposed method is evaluated on three attribute, classification joint datasets; ACCNN on the AC-AID dataset, the performance of ACCNN is suboptimal on AC-AID and is very close to the best method for DCA fusion. The average error rate of DCA fusion is 5.67%; on the AC-UCM dataset, the error rate of ACCNN is 2.11% lower than the suboptimal method DCA fusion, and the error rate of DCA fusion is 6.16%. On the AC-Sydney dataset, the error rate of ACCNN is 1.86% lower than the suboptimal method DCA fusion, and the error rate of DCA fusion is 5.99%, which obtains quite competitive results, experimenting with high-resolution images.