Modern object detectors take advantage of rectangular bounding boxes as a conventional way to represent objects. When it comes to fisheye images, rectangular boxes involve more background noise rather than semantic information. Although multi-point representation has been proposed, both the regression accuracy and convergence still perform inferior to the widely used rectangular boxes. In order to further exploit the advantages of multi-point representation for distorted images, Concentric Rectangles Regression Strategy(CRRS) is proposed in this work. We adopt smoother mean loss to allocate weights and discuss the effect of hyper-parameter to prediction results. Moreover, an accurate pixel-level method is designed to obtain irregular IoU for estimating detector performance. Compared with the previous work for muti-point representation, the experiments show that CRRS can improve the training performance both in accurate and stability. We also prove that multi-task weighting strategy facilitates regression process in this design.
@article{arxiv.2303.14639,
title = {CRRS: Concentric Rectangles Regression Strategy for Multi-point Representation on Fisheye Images},
author = {Xihan Wang and Xi Xu and Yu Gao and Yi Yang and Yufeng Yue and Mengyin Fu},
journal= {arXiv preprint arXiv:2303.14639},
year = {2023}
}