Semantic Labeling Based Vehicle Detection in Aerial Imagery

The increasing abundance of available aerial image and video data facilitates many applications, such as disaster relief, analysis of traffic flows, city planning, search tasks, and situation recognition. Automated detection systems that provide accurate detections of all relevant objects, e.g. vehicles, are essential for such applications. However, current detection frameworks, such as Faster R-CNN, are prone to cause false positive detections due to objects with shapes similar to vehicles, such as windows and solar panels on buildings. To address this issue, we propose two multi-task models that combine the detection task and a semantic labeling task, which induces more scene knowledge into the model. Through a shared global feature map we can improve detection results significantly. Additionally, by explicitly merging features of the semantic labeling branch into the region pooling step of the detection framework we can further reduce detection errors. We evaluate both models on the popular Potsdam dataset and outperform recent related work.