@inproceedings{
li2024adapkc,
title={Ada{PKC}: PeakConv with Adaptive Peak Receptive Field for Radar Semantic Segmentation},
author={Teng Li and Liwen Zhang and Youcheng Zhang and ZijunHu and Pengcheng Pi and Zongqing Lu and Qingmin Liao and Zhe Ma},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=oLcPadFrY3}
}
Deep learning-based radar detection technology is receiving increasing attention in areas such as autonomous driving, UAV surveillance, and marine monitoring. Among recent efforts, PeakConv (PKC) provides a solution that can retain the peak response characteristics of radar signals and play the characteristics of deep convolution, thereby improving the effect of radar semantic segmentation (RSS). However, due to the use of a pre-set fixed peak receptive field sampling rule, PKC still has limitations in dealing with problems such as inconsistency of target frequency domain response broadening, non-homogeneous and time-varying characteristic of noise/clutter distribution. Therefore, this paper proposes an idea of adaptive peak receptive field, and upgrades PKC to AdaPKC based on this idea. Beyond that, a novel fine-tuning technology to further boost the performance of AdaPKC-based RSS networks is presented. Through experimental verification using various real-measured radar data (including publicly available low-cost millimeter-wave radar dataset for autonomous driving and self-collected Ku-band surveillance radar dataset), we found that the performance of AdaPKC-based models surpasses other SoTA methods in RSS tasks. The code is available at https://github.com/lihua199710/AdaPKC.
AdaPKC: PeakConv with Adaptive Peak Receptive Field for Radar Semantic Segmentation
Teng Li Liwen Zhang Youcheng Zhang ZijunHu Pengcheng Pi Zongqing Lu Qingmin Liao Zhe Ma