Fuzzy-NMS: Improving 3D Object Detection with Fuzzy Classification in
NMS
- URL: http://arxiv.org/abs/2310.13951v1
- Date: Sat, 21 Oct 2023 09:09:03 GMT
- Title: Fuzzy-NMS: Improving 3D Object Detection with Fuzzy Classification in
NMS
- Authors: Li Wang, Xinyu Zhang, Fachuan Zhao, Chuze Wu, Yichen Wang, Ziying
Song, Lei Yang, Jun Li, Huaping Liu
- Abstract summary: Non-maximum suppression (NMS) is an essential post-processing module used in many 3D object detection frameworks.
We introduce fuzzy learning into NMS and propose a novel generalized Fuzzy-NMS module to achieve finer candidate bounding box filtering.
- Score: 19.452760776980472
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Non-maximum suppression (NMS) is an essential post-processing module used in
many 3D object detection frameworks to remove overlapping candidate bounding
boxes. However, an overreliance on classification scores and difficulties in
determining appropriate thresholds can affect the resulting accuracy directly.
To address these issues, we introduce fuzzy learning into NMS and propose a
novel generalized Fuzzy-NMS module to achieve finer candidate bounding box
filtering. The proposed Fuzzy-NMS module combines the volume and clustering
density of candidate bounding boxes, refining them with a fuzzy classification
method and optimizing the appropriate suppression thresholds to reduce
uncertainty in the NMS process. Adequate validation experiments are conducted
using the mainstream KITTI and large-scale Waymo 3D object detection
benchmarks. The results of these tests demonstrate the proposed Fuzzy-NMS
module can improve the accuracy of numerous recently NMS-based detectors
significantly, including PointPillars, PV-RCNN, and IA-SSD, etc. This effect is
particularly evident for small objects such as pedestrians and bicycles. As a
plug-and-play module, Fuzzy-NMS does not need to be retrained and produces no
obvious increases in inference time.
Related papers
- Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation [91.83820250747935]
Pseudo-label noise is mainly contained in unstable samples in which predictions of most pixels undergo significant variations during self-training.
We introduce the Stable Neighbor Denoising (SND) approach, which effectively discovers highly correlated stable and unstable samples.
SND consistently outperforms state-of-the-art methods in various SFUDA semantic segmentation settings.
arXiv Detail & Related papers (2024-06-10T21:44:52Z) - Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection [108.672972439282]
We introduce a novel decoupled pseudo-labeling (DPL) approach for SSM3OD.
Our approach features a Decoupled Pseudo-label Generation (DPG) module, designed to efficiently generate pseudo-labels.
We also present a DepthGradient Projection (DGP) module to mitigate optimization conflicts caused by noisy depth supervision of pseudo-labels.
arXiv Detail & Related papers (2024-03-26T05:12:18Z) - OccRob: Efficient SMT-Based Occlusion Robustness Verification of Deep
Neural Networks [7.797299214812479]
Occlusion is a prevalent and easily realizable semantic perturbation to deep neural networks (DNNs)
It can fool a DNN into misclassifying an input image by occluding some segments, possibly resulting in severe errors.
Most existing robustness verification approaches for DNNs are focused on non-semantic perturbations.
arXiv Detail & Related papers (2023-01-27T18:54:00Z) - Semi-supervised 3D Object Detection with Proficient Teachers [114.54835359657707]
Dominated point cloud-based 3D object detectors in autonomous driving scenarios rely heavily on the huge amount of accurately labeled samples.
Pseudo-Labeling methodology is commonly used for SSL frameworks, however, the low-quality predictions from the teacher model have seriously limited its performance.
We propose a new Pseudo-Labeling framework for semi-supervised 3D object detection, by enhancing the teacher model to a proficient one with several necessary designs.
arXiv Detail & Related papers (2022-07-26T04:54:03Z) - GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation [70.75100533512021]
In this paper, we formulate the label uncertainty problem as the diversity of potentially plausible bounding boxes of objects.
We propose GLENet, a generative framework adapted from conditional variational autoencoders, to model the one-to-many relationship between a typical 3D object and its potential ground-truth bounding boxes with latent variables.
The label uncertainty generated by GLENet is a plug-and-play module and can be conveniently integrated into existing deep 3D detectors.
arXiv Detail & Related papers (2022-07-06T06:26:17Z) - GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D
Object Detection [25.313894069303718]
We present and integrate GrooMeD-NMS -- a novel Grouped Mathematically Differentiable NMS for monocular 3D object detection.
GrooMeD-NMS addresses the mismatch between training and inference pipelines.
It achieves state-of-the-art monocular 3D object detection results on the KITTI benchmark dataset.
arXiv Detail & Related papers (2021-03-31T16:29:50Z) - Object Detection Made Simpler by Eliminating Heuristic NMS [70.93004137521946]
We show a simple NMS-free, end-to-end object detection framework.
We attain on par or even improved detection accuracy compared with the original one-stage detector.
arXiv Detail & Related papers (2021-01-28T02:38:29Z) - End-to-End Object Detection with Fully Convolutional Network [71.56728221604158]
We introduce a Prediction-aware One-To-One (POTO) label assignment for classification to enable end-to-end detection.
A simple 3D Max Filtering (3DMF) is proposed to utilize the multi-scale features and improve the discriminability of convolutions in the local region.
Our end-to-end framework achieves competitive performance against many state-of-the-art detectors with NMS on COCO and CrowdHuman datasets.
arXiv Detail & Related papers (2020-12-07T09:14:55Z) - ASAP-NMS: Accelerating Non-Maximum Suppression Using Spatially Aware
Priors [26.835571059909007]
Non Maximum Suppression (or Greedy-NMS) is a crucial module for object-detection pipelines.
For the region proposal stage of two/multi-stage detectors, NMS is turning out to be a latency bottleneck due to its sequential nature.
We use ASAP-NMS to improve the latency of the NMS step from 13.6ms to 1.2 ms on a CPU without sacrificing the accuracy of a state-of-the-art two-stage detector.
arXiv Detail & Related papers (2020-07-19T21:15:48Z) - Visibility Guided NMS: Efficient Boosting of Amodal Object Detection in
Crowded Traffic Scenes [7.998326245039892]
Modern 2D object detection frameworks predict multiple bounding boxes per object that are refined using Non-Maximum-Suppression (NMS) to suppress all but one bounding box.
Our novel Visibility Guided NMS (vg-NMS) leverages both pixel-based as well as amodal object detection paradigms and improves the detection performance especially for highly occluded objects with little computational overhead.
We evaluate vg-NMS using KITTI, VIPER as well as the Synscapes dataset and show that it outperforms current state-of-the-art NMS.
arXiv Detail & Related papers (2020-06-15T17:03:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.