UAV Detection using Web Application Approach based on SSD Pre-Trained Model

UAV development is being intensively developed by various groups to help overcome various types of problems. Object Detection is important in helping UAVs to do drone chasing and other competition that need visual approach based on image processing and deep learning. Unfortunately, the computational capabilities of the onboard processing unit that attached to the UAV are less than optimal for object detection due to storage and memory size constraints. This paper aims to create the new approach to improve the precision and recall during UAV detection by using web application to do real time detection. To decide a pre-trained model, it is necessary to compare which SSD pre-trained model is suitable to be deployed in this web application. The results obtained are that using the web application approach is better than the onboard processing approach with a high level of precision and recall with an average precision value of 0.85 and an average recall value of 0.837.