# Research on Automatic Recognition Method of Artificial Ground Target Based on Improved HED
###### Abstract  
Automatic target recognition technology is an important research direction in the field of machine vision. Artificial ground targets, such as bridges, airports and houses, are mostly composed of straight lines. The ratio of geometric primitive lines to the triangle area formed by their combination is used as the feature quantity to describe the group of lines, so as to characterize the artificial ground target. In view of the shortcomings of traditional edge detection methods, such as background suppression, non-prominent targets, missing positions, etc., this paper proposed an image edge detection method based on depth learning. By combining the traditional edge detection algorithm with the edge detection algorithm based on an improved HED network, the real-time target image edge detection was completed. An automatic target recognition method based on template matching was proposed. This method solved the problem of both homologous template matching and heterogeneous template matching, which has important theoretical value. First, the lines were combined to form the geometric primitives of the line group, and then the relationship of the lines in the group was determined by using the characteristic quantity of the line group. The best line group matching the target template was found in the image edge, and the homonymous points in the real-time image and the target template were calculated. The affine transformation matrix between the two images was obtained according to the homonymous points, and then the accurate position of the target in the real-time image was found. geometric primitive; deep learning; automatic target recognition; improve HED network HED. 2023133163. 2023133163. 2023133163. 2023133163. 2023133163. 2023133163. 202313163. At present, the research on the edge detection algorithm is still being updated. Reference [4] proposed an edge detection algorithm based on the lightweight module reconstruction model to obtain data features; Reference [5] proposed an optimized Canny edge detection algorithm and an edge detection algorithm based on depth learning; Reference [6] proposed an edge detection algorithm for color image graying; Reference [7] proposed an infrared image edge detection method based on the adaptive threshold method; Reference [8] proposed an improved Canny edge detection method; and in [9], a quantum image edge detection algorithm based on the classical Gauss-Laplace operator was proposed. In terms of edge detection, algorithms based on depth learning, such as Holistically Nested Edge Detection [10], DeepContour [11] and Deep Edge [12], have been proposed in recent years. The HED (Holistically Nested Edge Detection) algorithm is an end-to-end edge detection network [13]. The HED algorithm can perform multi-scale and multi-level learning on images and can directly operate the entire image, and it has a good effect on extracting the complex texture information of objects and backgrounds. Image feature extraction relies on high-level features with more semantic information in the HED network, but the resolution of high-level features in the HED network is very low, which leads to the loss of some information and affects the extraction of image objects. In order to solve this problem, the HED network was improved to make the output of high-level features more precise. At the same time, this algorithm was integrated with the traditional edge detection algorithm to improve the sensitivity and accuracy of location. Therefore, this paper designs an image edge detection method based on improved HED. Traditional automatic target recognition technology based on template matching usually requires navigation information to transform the scale and perspective of the template drawing, and the images obtained in different periods and environments are different. In order to solve the above problems, this paper designs an automatic target recognition method based on template matching without leader information according to the characteristics of affine transformation invariants.