Arbitrary-oriented traffic participant detection and axis prediction for complex crossing road in aerial view

Different from object detection in natural images, aerial-view object detection faces special challenges with large changes in object orientation and wide multi-scale distribution. Many methods based on Oriented Bounding Boxes (OBB) can reach accurate results, yet may face the problem of parameter mutation and axis prediction deviation. To address these problems, a two-stage detection framework is proposed for arbitrary-oriented traffic participant detection and axis prediction, named Axis Prediction Network. First, a Deformable Convolution Fusion Module (DCF-Module) is proposed to enhance the ability of FPN to extract multi-scale semantic features, for dealing with the multi-scale change of objects. Then, the axis heat-map prediction head network is proposed to fit the long axis of oriented objects labeled with Gaussian model, which uses pixel-by-pixel prediction heatmap to calculate the long axis of the object, avoiding the angle mutation of an OBB. Last, the long and short side prediction head network is proposed to predict the shape of an oriented object, avoiding the mutation of the width and height of an OBB. The experiments are conducted on the new built Crossroad dataset and the public DOTA dataset. Experiment results show that the proposed method achieve good performance in arbitrary-oriented traffic participant detection and axis prediction in aerial view.