Realization of Detection Algorithms for Key Parts of Unmanned Aerial Vehicle Based on Deep Learning

Fixed-point attack on the key parts of small aerial vehicles is one of the important means of UAV (Unmanned Aerial Vehicle) countermeasure. Fixed-wing aircraft flying in the air has two characteristics: fast speed and fast attitude change of aircraft. However, the traditional detection method of key parts of fixed-wing aircraft in infrared images is not only slow in speed but also low in accuracy. This paper presents an improved target detection and motion tracking algorithm based on deep learning method. The algorithm uses a deeper neural network to enhance the feature extraction capabilities of CNN and obtain richer feature information. The detection algorithm obtains the position and motion direction of the key points of the target. Experimental results show that the detection algorithm proposed in this paper is up to 30 frame/s, with an average accuracy of 91.5%/. The method achieved good results in the experiment.