This paper proposes a new irregular remote sensing object detection algorithm that different from the ROI or rotating BOX obtained by traditional one. The architecture is designed to jointly learn four bounding box corner points and their association via two branches of the same sequential prediction process. The algorithm predicts four key points of the object and their associated connection, Bounding Box Fields(BBF) via convolutional neural network(CNN), and thus obtains the detail spatial distribution of the objects.
In order to improve the positioning accuracy of the key points, network architecture reduced Receptive Field from large to small stage by stage. It has achieved ROI free finally. In this method, the object detection problem is framed as CNN convolution point detection and bounding box field detection, it achieved the one stage object detection with high precision and high speed.
We verified the effectiveness and efficiency of the algorithm through experiments, which proved that the new data structure could locate the object attitude and spatial direction more accurately in real time with strong practicability.