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CSL

Arbitrary-Oriented Object Detection with Circular Smooth Label

Abstract

Arbitrary-oriented object detection has recently attracted increasing attention in vision for their importance in aerial imagery, scene text, and face etc. In this paper, we show that existing regression-based rotation detectors suffer the problem of discontinuous boundaries, which is directly caused by angular periodicity or corner ordering. By a careful study, we find the root cause is that the ideal predictions are beyond the defined range. We design a new rotation detection baseline, to address the boundary problem by transforming angular prediction from a regression problem to a classification task with little accuracy loss, whereby high-precision angle classification is devised in contrast to previous works using coarse-granularity in rotation detection. We also propose a circular smooth label (CSL) technique to handle the periodicity of the angle and increase the error tolerance to adjacent angles. We further introduce four window functions in CSL and explore the effect of different window radius sizes on detection performance. Extensive experiments and visual analysis on two large-scale public datasets for aerial images i.e. DOTA, HRSC2016, as well as scene text dataset ICDAR2015 and MLT, show the effectiveness of our approach.

Results and models

DOTA1.0

Backbone mAP Angle Window func. Omega lr schd Mem (GB) Inf Time (fps) Aug Batch Size Configs Download
ResNet50 (1024,1024,200) 68.42 le90 - - 1x 3.38 17.8 - 2 rotated_retinanet_obb_r50_fpn_1x_dota_le90 model | log
ResNet50 (1024,1024,200) 68.79 le90 - - 1x 2.36 25.9 - 2 rotated_retinanet_obb_r50_fpn_fp16_1x_dota_le90 model | log
ResNet50 (1024,1024,200) 69.51 le90 Gaussian 4 1x 2.60 24.0 - 2 rotated_retinanet_obb_csl_gaussian_r50_fpn_fp16_1x_dota_le90 model | log

Citation

@inproceedings{yang2020arbitrary,
    title={Arbitrary-Oriented Object Detection with Circular Smooth Label},
    author={Yang, Xue and Yan, Junchi},
    booktitle={European Conference on Computer Vision},
    pages={677--694},
    year={2020}
}