NIV-SSD: Neighbor IoU-Voting Single-Stage Object Detector From Point Cloud

• URL: http://arxiv.org/abs/2401.12447v1
• Date: Tue, 23 Jan 2024 02:25:23 GMT
• Title: NIV-SSD: Neighbor IoU-Voting Single-Stage Object Detector From Point Cloud
• Authors: Shuai Liu, Di Wang, Quan Wang, Kai Huang
• Abstract summary: Previous single-stage detectors typically suffer the misalignment between localization accuracy and classification confidence. We introduce a novel rectification method named neighbor IoU-voting (NIV) strategy. To alleviate the imbalance of detection accuracy for complete objects with dense points, we propose a new data augmentation scheme named object resampling.
• Score: 17.134983202997034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Previous single-stage detectors typically suffer the misalignment between localization accuracy and classification confidence. To solve the misalignment problem, we introduce a novel rectification method named neighbor IoU-voting (NIV) strategy. Typically, classification and regression are treated as separate branches, making it challenging to establish a connection between them. Consequently, the classification confidence cannot accurately reflect the regression quality. NIV strategy can serve as a bridge between classification and regression branches by calculating two types of statistical data from the regression output to correct the classification confidence. Furthermore, to alleviate the imbalance of detection accuracy for complete objects with dense points (easy objects) and incomplete objects with sparse points (difficult objects), we propose a new data augmentation scheme named object resampling. It undersamples easy objects and oversamples difficult objects by randomly transforming part of easy objects into difficult objects. Finally, combining the NIV strategy and object resampling augmentation, we design an efficient single-stage detector termed NIV-SSD. Extensive experiments on several datasets indicate the effectiveness of the NIV strategy and the competitive performance of the NIV-SSD detector. The code will be available at https://github.com/Say2L/NIV-SSD.

Related papers

• Similarity Distance-Based Label Assignment for Tiny Object Detection [17.059514012235354]
  We introduce a simple but effective strategy named the Similarity Distance (SimD) to evaluate the similarity between bounding boxes. Our approach can be applied in common anchor-based detectors in place of the IoU for label assignment and Non Maximum Suppression (NMS) Experiments on four mainstream tiny object detection datasets demonstrate superior performance of our method.
  arXiv  Detail & Related papers  (2024-07-02T16:12:37Z)
• Better Sampling, towards Better End-to-end Small Object Detection [7.7473020808686694]
  Small object detection remains unsatisfactory due to limited characteristics and high density and mutual overlap. We propose methods enhancing sampling within an end-to-end framework. Our model demonstrates a significant enhancement, achieving a 2.9% increase in average precision (AP) over the state-of-the-art (SOTA) on the VisDrone dataset.
  arXiv  Detail & Related papers  (2024-05-17T04:37:44Z)
• Find n' Propagate: Open-Vocabulary 3D Object Detection in Urban Environments [67.83787474506073]
  We tackle the limitations of current LiDAR-based 3D object detection systems. We introduce a universal textscFind n' Propagate approach for 3D OV tasks. We achieve up to a 3.97-fold increase in Average Precision (AP) for novel object classes.
  arXiv  Detail & Related papers  (2024-03-20T12:51:30Z)
• SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection [53.19618419772467]
  Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. With the development of Transformer, the scale of SIRST models is constantly increasing. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed.
  arXiv  Detail & Related papers  (2024-03-08T16:14:54Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images [15.404024559652534]
  We present an effective Dynamic Enhancement Anchor (DEA) network to construct a novel training sample generator. Our method achieves state-of-the-art performance in accuracy with moderate inference speed and computational overhead for training.
  arXiv  Detail & Related papers  (2021-12-13T14:37:20Z)
• CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud [40.871850077528485]
  We present a new single-stage detector named the Confident IoU-Aware Single-Stage object Detector (CIA-SSD) CIA-SSD adaptively fuses high-level abstract semantic features and low-level spatial features for accurate predictions of bounding boxes and classification confidence. It attains top performance in terms of the official ranking metric and above 32 FPS inference speed.
  arXiv  Detail & Related papers  (2020-12-05T12:00:08Z)
• Multi-scale Interactive Network for Salient Object Detection [91.43066633305662]
  We propose the aggregate interaction modules to integrate the features from adjacent levels. To obtain more efficient multi-scale features, the self-interaction modules are embedded in each decoder unit. Experimental results on five benchmark datasets demonstrate that the proposed method without any post-processing performs favorably against 23 state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-07-17T15:41:37Z)
• Cascaded Regression Tracking: Towards Online Hard Distractor Discrimination [202.2562153608092]
  We propose a cascaded regression tracker with two sequential stages. In the first stage, we filter out abundant easily-identified negative candidates. In the second stage, a discrete sampling based ridge regression is designed to double-check the remaining ambiguous hard samples.
  arXiv  Detail & Related papers  (2020-06-18T07:48:01Z)
• Scope Head for Accurate Localization in Object Detection [135.9979405835606]
  We propose a novel detector coined as ScopeNet, which models anchors of each location as a mutually dependent relationship. With our concise and effective design, the proposed ScopeNet achieves state-of-the-art results on COCO.
  arXiv  Detail & Related papers  (2020-05-11T04:00:09Z)
• Seeing without Looking: Contextual Rescoring of Object Detections for AP Maximization [4.346179456029563]
  We propose to incorporate context in object detection by post-processing the output of an arbitrary detector. Rescoring is done by conditioning on contextual information from the entire set of detections. We show that AP can be improved by simply reassigning the detection confidence values.
  arXiv  Detail & Related papers  (2019-12-27T18:56:29Z)

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.