Active Terahertz Imaging Dataset for Concealed Object Detection
- URL: http://arxiv.org/abs/2105.03677v1
- Date: Sat, 8 May 2021 11:21:38 GMT
- Title: Active Terahertz Imaging Dataset for Concealed Object Detection
- Authors: Dong Liang, Fei Xue and Ling Li
- Abstract summary: This paper provides a public dataset for evaluating multi-object detection algorithms in Terahertz imaging resolution 5 mm by 5 mm.
We evaluate four popular detectors: YOLOv3, YOLOv4, FRCN-OHEM, and RetinaNet.
Experimental results indicate that the RetinaNet achieves the highest mAP.
- Score: 16.26153671724079
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Concealed object detection in Terahertz imaging is an urgent need for public
security and counter-terrorism. In this paper, we provide a public dataset for
evaluating multi-object detection algorithms in active Terahertz imaging
resolution 5 mm by 5 mm. To the best of our knowledge, this is the first public
Terahertz imaging dataset prepared to evaluate object detection algorithms.
Object detection on this dataset is much more difficult than on those standard
public object detection datasets due to its inferior imaging quality. Facing
the problem of imbalanced samples in object detection and hard training
samples, we evaluate four popular detectors: YOLOv3, YOLOv4, FRCN-OHEM, and
RetinaNet on this dataset. Experimental results indicate that the RetinaNet
achieves the highest mAP. In addition, we demonstrate that hiding objects in
different parts of the human body affect detection accuracy. The dataset is
available at https://github.com/LingLIx/THz_Dataset.
Related papers
- Bayesian Detector Combination for Object Detection with Crowdsourced Annotations [49.43709660948812]
Acquiring fine-grained object detection annotations in unconstrained images is time-consuming, expensive, and prone to noise.
We propose a novel Bayesian Detector Combination (BDC) framework to more effectively train object detectors with noisy crowdsourced annotations.
BDC is model-agnostic, requires no prior knowledge of the annotators' skill level, and seamlessly integrates with existing object detection models.
arXiv Detail & Related papers (2024-07-10T18:00:54Z) - FlightScope: A Deep Comprehensive Review of Aircraft Detection Algorithms in Satellite Imagery [2.9687381456164004]
This paper critically evaluates and compares a suite of advanced object detection algorithms customized for the task of identifying aircraft within satellite imagery.
This research encompasses an array of methodologies including YOLO versions 5 and 8, Faster RCNN, CenterNet, RetinaNet, RTMDet, and DETR, all trained from scratch.
YOLOv5 emerges as a robust solution for aerial object detection, underlining its importance through superior mean average precision, Recall, and Intersection over Union scores.
arXiv Detail & Related papers (2024-04-03T17:24:27Z) - Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for
Advanced Object Detection [55.2480439325792]
We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head.
The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement.
This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
arXiv Detail & Related papers (2023-11-21T19:49:13Z) - SalienDet: A Saliency-based Feature Enhancement Algorithm for Object
Detection for Autonomous Driving [160.57870373052577]
We propose a saliency-based OD algorithm (SalienDet) to detect unknown objects.
Our SalienDet utilizes a saliency-based algorithm to enhance image features for object proposal generation.
We design a dataset relabeling approach to differentiate the unknown objects from all objects in training sample set to achieve Open-World Detection.
arXiv Detail & Related papers (2023-05-11T16:19:44Z) - A Multi-purpose Real Haze Benchmark with Quantifiable Haze Levels and
Ground Truth [61.90504318229845]
This paper introduces the first paired real image benchmark dataset with hazy and haze-free images, and in-situ haze density measurements.
This dataset was produced in a controlled environment with professional smoke generating machines that covered the entire scene.
A subset of this dataset has been used for the Object Detection in Haze Track of CVPR UG2 2022 challenge.
arXiv Detail & Related papers (2022-06-13T19:14:06Z) - Focus-and-Detect: A Small Object Detection Framework for Aerial Images [1.911678487931003]
We propose a two-stage object detection framework called "Focus-and-Detect"
The first stage generates clusters of objects constituting the focused regions.
The second stage, which is also an object detector network, predicts objects within the focal regions.
Results indicate that the proposed two-stage framework achieves an AP score of 42.06 on VisDrone validation dataset.
arXiv Detail & Related papers (2022-03-24T10:43:56Z) - Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection [2.578242050187029]
Slicing Aided Hyper Inference (SAHI) is proposed that provides a generic slicing aided inference and fine-tuning pipeline for small object detection.
Proposed technique has been integrated with Detectron2, MMDetection and YOLOv5 models.
arXiv Detail & Related papers (2022-02-14T18:49:12Z) - Remote Sensing Image Super-resolution and Object Detection: Benchmark
and State of the Art [7.74389937337756]
This paper reviews current datasets and object detection methods (deep learning-based) for remote sensing images.
We propose a large-scale, publicly available benchmark Remote Sensing Super-resolution Object Detection dataset.
We also propose a novel Multi-class Cyclic super-resolution Generative adversarial network with Residual feature aggregation (MCGR) and auxiliary YOLOv5 detector to benchmark image super-resolution-based object detection.
arXiv Detail & Related papers (2021-11-05T04:56:34Z) - FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in
High-Resolution Remote Sensing Imagery [21.9319970004788]
We propose a novel benchmark dataset with more than 1 million instances and more than 15,000 images for Fine-grAined object recognItion in high-Resolution remote sensing imagery.
All objects in the FAIR1M dataset are annotated with respect to 5 categories and 37 sub-categories by oriented bounding boxes.
arXiv Detail & Related papers (2021-03-09T17:20:15Z) - Ensembling object detectors for image and video data analysis [98.26061123111647]
We propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data.
We extend it to video data by proposing a two-stage tracking-based scheme for detection refinement.
arXiv Detail & Related papers (2021-02-09T12:38:16Z) - TJU-DHD: A Diverse High-Resolution Dataset for Object Detection [48.94731638729273]
Large-scale, rich-diversity, and high-resolution datasets play an important role in developing better object detection methods.
We build a diverse high-resolution dataset (called TJU-DHD)
The dataset contains 115,354 high-resolution images and 709,330 labeled objects with a large variance in scale and appearance.
arXiv Detail & Related papers (2020-11-18T09:32:24Z)
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.