From COCO to COCO-FP: A Deep Dive into Background False Positives for COCO Detectors

• URL: http://arxiv.org/abs/2409.07907v1
• Date: Thu, 12 Sep 2024 10:22:12 GMT
• Title: From COCO to COCO-FP: A Deep Dive into Background False Positives for COCO Detectors
• Authors: Longfei Liu, Wen Guo, Shihua Huang, Cheng Li, Xi Shen,
• Abstract summary: False alarms in fire and smoke detection are critical in real-world applications. COCO-FP is a new evaluation dataset derived from the ImageNet-1K dataset. Our evaluation of both standard and advanced object detectors shows a significant number of false positives in both closed-set and open-set scenarios.
• Score: 8.3561487803637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reducing false positives is essential for enhancing object detector performance, as reflected in the mean Average Precision (mAP) metric. Although object detectors have achieved notable improvements and high mAP scores on the COCO dataset, analysis reveals limited progress in addressing false positives caused by non-target visual clutter-background objects not included in the annotated categories. This issue is particularly critical in real-world applications, such as fire and smoke detection, where minimizing false alarms is crucial. In this study, we introduce COCO-FP, a new evaluation dataset derived from the ImageNet-1K dataset, designed to address this issue. By extending the original COCO validation dataset, COCO-FP specifically assesses object detectors' performance in mitigating background false positives. Our evaluation of both standard and advanced object detectors shows a significant number of false positives in both closed-set and open-set scenarios. For example, the AP50 metric for YOLOv9-E decreases from 72.8 to 65.7 when shifting from COCO to COCO-FP. The dataset is available at https://github.com/COCO-FP/COCO-FP.

Related papers

• 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)
• Benchmarking Object Detectors with COCO: A New Path Forward [26.754253266204184]
  The Common Objects in Context (COCO) dataset has been instrumental in benchmarking object detectors over the past decade. With the advent of high-labeled models, we ask whether these errors of COCO are hindering its utility in reliably benchmarking object detectors. We uncover different types of errors such as imprecise mask boundaries, non-exhaustively annotated instances, and misperforming masks.
  arXiv  Detail & Related papers  (2024-03-27T17:59:53Z)
• Class Imbalance in Object Detection: An Experimental Diagnosis and Study of Mitigation Strategies [0.5439020425818999]
  This study introduces a benchmarking framework utilizing the YOLOv5 single-stage detector to address the problem of foreground-foreground class imbalance. We scrutinized three established techniques: sampling, loss weighing, and data augmentation. Our comparative analysis reveals that sampling and loss reweighing methods, while shown to be beneficial in two-stage detector settings, do not translate as effectively in improving YOLOv5's performance.
  arXiv  Detail & Related papers  (2024-03-11T19:06:04Z)
• COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts [27.406639379618003]
  COCO-O is a test dataset based on COCO with 6 types of natural distribution shifts. COCO-O has a large distribution gap with training data and results in a significant 55.7% relative performance drop on a Faster R-CNN detector. We study the robustness effect on recent breakthroughs of detector's architecture design, augmentation and pre-training techniques.
  arXiv  Detail & Related papers  (2023-07-24T12:22:19Z)
• Co-Salient Object Detection with Co-Representation Purification [80.2644026634024]
  Co-salient object detection (Co-SOD) aims at discovering the common objects in a group of relevant images. The current Co-SOD method does not pay enough attention that the information not related to the co-salient object is included in the co-representation. We propose a Co-Representation Purification (CoRP) method aiming at searching noise-free co-representation.
  arXiv  Detail & Related papers  (2023-03-14T07:23:27Z)
• Object Localization under Single Coarse Point Supervision [107.46800858130658]
  We propose a POL method using coarse point annotations, relaxing the supervision signals from accurate key points to freely spotted points. CPR constructs point bags, selects semantic-correlated points, and produces semantic center points through multiple instance learning (MIL) In this way, CPR defines a weakly supervised evolution procedure, which ensures training high-performance object localizer under coarse point supervision.
  arXiv  Detail & Related papers  (2022-03-17T14:14:11Z)
• WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection [75.80075054706079]
  We propose a weakly- and semi-supervised object detection framework (WSSOD) An agent detector is first trained on a joint dataset and then used to predict pseudo bounding boxes on weakly-annotated images. The proposed framework demonstrates remarkable performance on PASCAL-VOC and MSCOCO benchmark, achieving a high performance comparable to those obtained in fully-supervised settings.
  arXiv  Detail & Related papers  (2021-05-21T11:58:50Z)
• Detection in Crowded Scenes: One Proposal, Multiple Predictions [79.28850977968833]
  We propose a proposal-based object detector, aiming at detecting highly-overlapped instances in crowded scenes. The key of our approach is to let each proposal predict a set of correlated instances rather than a single one in previous proposal-based frameworks. Our detector can obtain 4.9% AP gains on challenging CrowdHuman dataset and 1.0% $textMR-2$ improvements on CityPersons dataset.
  arXiv  Detail & Related papers  (2020-03-20T09:48:53Z)
• Learning Delicate Local Representations for Multi-Person Pose Estimation [77.53144055780423]
  We propose a novel method called Residual Steps Network (RSN) RSN aggregates features with the same spatial size (Intra-level features) efficiently to obtain delicate local representations. Our approach won the 1st place of COCO Keypoint Challenge 2019.
  arXiv  Detail & Related papers  (2020-03-09T10:40:49Z)
• Solving Missing-Annotation Object Detection with Background Recalibration Loss [49.42997894751021]
  This paper focuses on a novel and challenging detection scenario: A majority of true objects/instances is unlabeled in the datasets. Previous art has proposed to use soft sampling to re-weight the gradients of RoIs based on the overlaps with positive instances, while their method is mainly based on the two-stage detector. In this paper, we introduce a superior solution called Background Recalibration Loss (BRL) that can automatically re-calibrate the loss signals according to the pre-defined IoU threshold and input image.
  arXiv  Detail & Related papers  (2020-02-12T23:11:46Z)

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.