Research on air target recognition method based on deep learning

Air target recognition in real-world scenarios has become an important part of the military offensive and defensive systems of various countries. By identifying aerial targets captured by image acquisition equipment and utilizing the obtained information to achieve effective identification of friend or foe, identifying enemy sources, combat capabilities, and intentions, important references are provided for tactical decision-making. With the continuous development of military technology, traditional manual based recognition methods are no longer capable of identifying aerial targets. The role of deep learning related algorithms, which gradually replace traditional image processing algorithms, in the field of target recognition is becoming increasingly prominent. This article will use deep learning methods to study aerial target recognition. Provide a detailed description of the design and implementation process of the Faster R-CNN training model, including model structure, network layers, and feature selection. This model mainly includes a Region Proposal Network (RPN) and a set of convolutional neural networks for extracting target features and predicting target bounding boxes. Build a basic environment for training models, and establish a dataset of air targets related to military equipment. The dataset samples are annotated according to the VOC2007 dataset format. Train the model using a dataset, conduct testing and detection after training, and finally analyze the calculation results of Eval evaluation indicators to further optimize the algorithm model and improve recognition rate. The experiment verified the effectiveness and feasibility of the Faster R-CNN training model, and applied it to aerial target recognition tasks. The experimental results show that the model can quickly and accurately identify aerial targets.