Armed Aircraft Detection and Identification Web Application using Deep Learning Techniques and Flask

In Contemporary situation military applications, recognizing armed aircraft is crucial for making strategic decisions. The challenge is in accurately recognizing the unidentified aircraft regardless of its direction. This paper represents a research project in this area. Rapidly created physical models of 12 classes of armed aircraft were utilized to obtain the database used here. The residents who live close to the border have recently noticed various types of aircraft flying overhead, but they are unaware of whether the aircraft is armed or not. The citizens will be assisted by this project in recognizing armed aircraft and their type. If people notice a specific armed aircraft flying overhead regularly, they should report it to the local authorities along with the aircraft’s model name so that they may determine whether the aircraft is part of their prospective Airforce or not and take appropriate action. A neural network predicts items in a picture and identifies them using bounding boxes in object detection, a sophisticated type of image classification. Object detection, also known as object recognition, is a crucial subfield in computer vision because tasks like detection, localization have broad applications in real-world contexts. For identifying the Armed aircraft Yolo V7 Algorithm is implemented from deep learning for instance