Fisheye object detection based on standard image datasets with 24-points regression strategy

Fisheye object detection is a difficult task in robotics and autonomous driving. One of the reasons is that the fisheye datasets are inferior to standard image datasets in scale and quantity, which inspires the idea of using standard image datasets for fisheye object detection. However, the models trained on standard image datasets do not perform well with fisheye data. In this work, we explore the effect of fisheye images on different stages of the YOLOX with published weights generated by standard image datasets. We also propose a new regression strategy for 24-points object representation method, which is insensitive to image distortion. The experiments show that the feature extraction part is robust to fisheye image features, while the regression part of location and category performs poorly. The strategy can achieve the position of discrete points without calculating the IOU of irregular-shaped boxes. Theoretically, the strategy can be widely adopted to regress the irregular bounding boxes composed of discrete points. Source code is at https://github.com/IN2-ViAUn/Exploration-of-Potential.