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Traffic density, which is a critical measure in traffic operations, should be collected precisely at various locations and times to reflect site-specific spatiotemporal characteristics. For detailed analysis, heavy vehicles have to be separated from ordinary vehicles, since heavy vehicles have a significant effect on traffic flow as well as traffic safety. With unmanned aerial vehicles (UAVs), it is easy to acquire video for vehicle detection by collecting images from above the traffic without any disturbances. Despite previous studies on vehicle detection, there is still a lack of research on real-world applications in estimating traffic density. This study investigates the effects of several influential factors: the size of objects, the number of samples, and a combination of datasets, on detecting multi-class vehicles using deep learning models in various UAV images. Three detection models are compared: faster region-based convolutional …