SpecDETR: A Transformer-based Hyperspectral Point Object Detection Network

• URL: http://arxiv.org/abs/2405.10148v1
• Date: Thu, 16 May 2024 14:45:06 GMT
• Title: SpecDETR: A Transformer-based Hyperspectral Point Object Detection Network
• Authors: Zhaoxu Li, Wei An, Gaowei Guo, Longguang Wang, Yingqian Wang, Zaiping Lin,
• Abstract summary: Hyperspectral target detection (HTD) aims to identify materials based on spectral information in hyperspectral imagery and can detect point targets. Existing HTD methods are developed based on per-pixel binary classification, which limits the feature representation capability for point targets. We propose the first specialized network for hyperspectral multi-class point object detection, SpecDETR. We develop a simulated hyperSpectral Point Object Detection benchmark termed SPOD, and for the first time, evaluate and compare the performance of current object detection networks and HTD methods on hyperspectral multi-class point object detection.
• Score: 32.7318504162588
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hyperspectral target detection (HTD) aims to identify specific materials based on spectral information in hyperspectral imagery and can detect point targets, some of which occupy a smaller than one-pixel area. However, existing HTD methods are developed based on per-pixel binary classification, which limits the feature representation capability for point targets. In this paper, we rethink the hyperspectral point target detection from the object detection perspective, and focus more on the object-level prediction capability rather than the pixel classification capability. Inspired by the token-based processing flow of Detection Transformer (DETR), we propose the first specialized network for hyperspectral multi-class point object detection, SpecDETR. Without the backbone part of the current object detection framework, SpecDETR treats the spectral features of each pixel in hyperspectral images as a token and utilizes a multi-layer Transformer encoder with local and global coordination attention modules to extract deep spatial-spectral joint features. SpecDETR regards point object detection as a one-to-many set prediction problem, thereby achieving a concise and efficient DETR decoder that surpasses the current state-of-the-art DETR decoder in terms of parameters and accuracy in point object detection. We develop a simulated hyperSpectral Point Object Detection benchmark termed SPOD, and for the first time, evaluate and compare the performance of current object detection networks and HTD methods on hyperspectral multi-class point object detection. SpecDETR demonstrates superior performance as compared to current object detection networks and HTD methods on the SPOD dataset. Additionally, we validate on a public HTD dataset that by using data simulation instead of manual annotation, SpecDETR can detect real-world single-spectral point objects directly.

Related papers

• OPEN: Object-wise Position Embedding for Multi-view 3D Object Detection [102.0744303467713]
  We propose a new multi-view 3D object detector named OPEN. Our main idea is to effectively inject object-wise depth information into the network through our proposed object-wise position embedding. OPEN achieves a new state-of-the-art performance with 64.4% NDS and 56.7% mAP on the nuScenes test benchmark.
  arXiv  Detail & Related papers  (2024-07-15T14:29:15Z)
• Learning Remote Sensing Object Detection with Single Point Supervision [17.12725535531483]
  Pointly Supervised Object Detection (PSOD) has attracted considerable interests due to its lower labeling cost as compared to box-level supervised object detection. We make the first attempt to achieve RS object detection with single point supervision, and propose a PSOD method tailored for RS images. Our method can achieve significantly better performance as compared to state-of-the-art image-level and point-level supervised detection methods, and reduce the performance gap between PSOD and box-level supervised object detection.
  arXiv  Detail & Related papers  (2023-05-23T15:06:04Z)
• D-Align: Dual Query Co-attention Network for 3D Object Detection Based on Multi-frame Point Cloud Sequence [8.21339007493213]
  Conventional 3D object detectors detect objects using a set of points acquired over a fixed duration. Recent studies have shown that the performance of object detection can be further enhanced by utilizing point cloud sequences. We propose D-Align, which can effectively produce strong bird's-eye-view (BEV) features by aligning and aggregating the features obtained from a sequence of point sets.
  arXiv  Detail & Related papers  (2022-09-30T20:41:25Z)
• Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection [84.52197307286681]
  We propose a novel multitask auto encoding transformation (MAET) model to enhance object detection in a dark environment. In a self-supervision manner, the MAET learns the intrinsic visual structure by encoding and decoding the realistic illumination-degrading transformation. We have achieved the state-of-the-art performance using synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2022-05-06T16:27:14Z)
• RBGNet: Ray-based Grouping for 3D Object Detection [104.98776095895641]
  We propose the RBGNet framework, a voting-based 3D detector for accurate 3D object detection from point clouds. We propose a ray-based feature grouping module, which aggregates the point-wise features on object surfaces using a group of determined rays. Our model achieves state-of-the-art 3D detection performance on ScanNet V2 and SUN RGB-D with remarkable performance gains.
  arXiv  Detail & Related papers  (2022-04-05T14:42:57Z)
• A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation [9.513467995188634]
  3D object detection using LiDAR data is an indispensable component for autonomous driving systems. We propose a novel multi-task framework that jointly performs 3D object detection and panoptic segmentation.
  arXiv  Detail & Related papers  (2022-03-04T04:57:05Z)
• Single-stage Rotate Object Detector via Two Points with Solar Corona Heatmap [16.85421977235311]
  We develop a single-stage rotating object detector via two points with a solar corona heatmap to detect oriented objects. The ROTP predicts parts of the object and then aggregates them to form a whole image.
  arXiv  Detail & Related papers  (2022-02-14T09:07:21Z)
• SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection [78.90102636266276]
  We propose a novel set abstraction method named Semantics-Augmented Set Abstraction (SASA) Based on the estimated point-wise foreground scores, we then propose a semantics-guided point sampling algorithm to help retain more important foreground points during down-sampling. In practice, SASA shows to be effective in identifying valuable points related to foreground objects and improving feature learning for point-based 3D detection.
  arXiv  Detail & Related papers  (2022-01-06T08:54:47Z)
• LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention [100.52873557168637]
  3D object detectors usually focus on the single-frame detection, while ignoring the information in consecutive point cloud frames. In this paper, we propose an end-to-end online 3D video object detector that operates on point sequences.
  arXiv  Detail & Related papers  (2020-04-03T06:06:52Z)
• Plug & Play Convolutional Regression Tracker for Video Object Detection [37.47222104272429]
  Video object detection targets to simultaneously localize the bounding boxes of the objects and identify their classes in a given video. One challenge for video object detection is to consistently detect all objects across the whole video. We propose a Plug & Play scale-adaptive convolutional regression tracker for the video object detection task.
  arXiv  Detail & Related papers  (2020-03-02T15:57:55Z)
• PPDM: Parallel Point Detection and Matching for Real-time Human-Object Interaction Detection [85.75935399090379]
  We propose a single-stage Human-Object Interaction (HOI) detection method that has outperformed all existing methods on HICO-DET dataset at 37 fps. It is the first real-time HOI detection method.
  arXiv  Detail & Related papers  (2019-12-30T12:00:55Z)

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.