Affiliations: [a] School of Electronic Engineering, Chaohu University, Hefei, China
| [b] School of Physics and Electronic Engineering, Shanxi University, Taiyuan, China
Correspondence:
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Corresponding author. Haiyang Shen, School of Electronic Engineering, Chaohu University, Hefei 238024, China. E-mail: [email protected].
Abstract: In order to solve the problems with the traditional aircraft target type recognition algorithm, such as difficulty in feature selection, weak generalization ability, slow recognition speed, and low recognition accuracy, this paper put forward a new method that could detect and recognize aircraft targets in aerial images quickly and accurately. The aircraft targets in the images were detected rapidly and located through YOLOv3-tiny, and after image denoising, shadow detection, and positioning, then we used the Sobel operator to calculate the edge gradient of the target; the image of the aircraft target was segmented by using the region growth method, and then the principal component analysis (PCA)was used to obtain the central axis of the aircraft target. The projected distance from the edge contour to the central axis was sampled at equal intervals along the direction of the central axis, and its ratio to the length of the central axis was calculated to construct the feature vector. Finally, the Spearman rank correlation method was used to match the feature vectors to realize the recognition of the aircraft type. Experiments showed that the proposed method had strong adaptability and small computation and could quickly detect and accurately recognize aircraft targets in aerial images.
Keywords: Deep learning, aircraft identification, principal component analysis, spearman rank correlation
