Simplified LSL-Net Architecture for Unmanned Aerial Vehicle Detection in Real-Time

Given the growth of unmanned aerial vehicles (UAVs), their detection has become a recent and complex problem. The literature has addressed this problem by applying traditional computer vision algorithms and, more recently, deep learning architectures, which, while proven more effective than previous ones, are computationally more expensive. In this paper, following the approach of applying deep learning architectures, we propose a simplified LSL-Net-based architecture for UAV detection. This architecture integrates the ability to track and detect UAVs using convolutional neural networks. The biggest challenge lies in creating a model that allows us to obtain good results without requiring considerable computational resources. To address this problem, we built on a recent successful LSL-Net architecture. We introduce a simplified LSL-Net architecture using dilated convolutions to achieve a lower-cost architecture with good detection capabilities. Experiments demonstrate that our architecture performs well with limited resources, reaching 98% accuracy in detecting UAVs.