FPGA Based Military Vehicle Classification from Drone-Based Video Using Deep Learning

Remotely controlled aerial vehicles such as drones are used for military applications such as surveillance, intelligence and target acquisition. Real-time object detection and classification is one of the most recurrent tasks for drones. The proposed system is used to classify the military vehicles from the drone-based videos. Object segmentation and classification of military vehicles (military tanks and APCs) is done using the Detectron2 and is based on Mask-RCNN benchmark. A custom dataset consisting of 300 images of Military Tanks and Armored Personnel Carriers (APCs) has been created and annotated for the purpose of the project. Furthermore, the Mask RCNN (Resnet50+FPN) model is trained with the custom dataset to classify the vehicles. The trained model is integrated in to Field Programmable Gate Arrays (FPGA), to carry out onboard classification in a real-time manner. Performance measures are used to evaluate the proposed model and overall accuracy is 93% with reduced false positive rate.