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Vehicle detection based on deep learning has been developed rapidly and basically formed a certain pattern. Almost all works in vehicle detection are concentrated on single-label object detection. However, in the real world, a vehicle has multiple attributes from the perspective of a human being. When we observe a car, we tend to perceive its type, color, orientation, and other attributes. The categories of these labels are neither relevant nor hierarchical. For a neural network, this means that it needs to output multiple labels of a vehicle while regressing its bounding box. For traffic supervisors, a multi-label vehicle detection system could help them find out the targeted vehicle efficiently. Moreover, there has been no research on multi-label vehicle detection so far. Therefore, we design and develop a unified multi-label vehicle detection framework called ML-VehicleDet, which can detect the location (bounding box …