UAV-Net: A Fast Aerial Vehicle Detector for Mobile Platforms
Vehicle detection in aerial imagery is a challenging task due to small object sizes, high object density and partial occlusions. While past research mostly focused on improving detection accuracy, inference speed is another important factor when using CNN object detectors in a real life scenario - especially when targeting mobile platforms like unmanned aerial vehicles (UAVs). In this work, we compare several established detection frameworks in terms of their accuracy-speed trade-off and show that the Single Shot MultiBox Detector (SSD) offers the best compromise. We subsequently undertake a thorough evaluation of several design choices to further increase detection speed while sacrificing little to no accuracy. This includes the choice of base network architecture, improved prediction layers and an automatic model pruning approach. Given our evaluation results, we finally construct UAV-Net - a novel aerial vehicle detector that has a model size of less than 0.4 MiB and is more than 16 times faster than current top performing approaches. UAV-Net is well suited for on-board processing and operates in real time on a Jetson TX2 platform. Nevertheless, its accuracy is on par with state-of-the-art approaches on the DLR 3K, VEDAI and UAVDT datasets. Code and models are available on the project website.