Benchmarking performance of object detection under image distortions in an uncontrolled environment

• URL: http://arxiv.org/abs/2210.15999v1
• Date: Fri, 28 Oct 2022 09:06:52 GMT
• Title: Benchmarking performance of object detection under image distortions in an uncontrolled environment
• Authors: Ayman Beghdadi, Malik Mallem, Lotfi Beji
• Abstract summary: robustness of object detection algorithms plays a prominent role in real-world applications. It has been proven that the performance of object detection methods suffers from in-capture distortions. We present a performance evaluation framework for the state-of-the-art object detection methods.
• Score: 0.483420384410068
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The robustness of object detection algorithms plays a prominent role in real-world applications, especially in uncontrolled environments due to distortions during image acquisition. It has been proven that the performance of object detection methods suffers from in-capture distortions. In this study, we present a performance evaluation framework for the state-of-the-art object detection methods using a dedicated dataset containing images with various distortions at different levels of severity. Furthermore, we propose an original strategy of image distortion generation applied to the MS-COCO dataset that combines some local and global distortions to reach much better performances. We have shown that training using the proposed dataset improves the robustness of object detection by 31.5\%. Finally, we provide a custom dataset including natural images distorted from MS-COCO to perform a more reliable evaluation of the robustness against common distortions. The database and the generation source codes of the different distortions are made publicly available

Related papers

• RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection [4.231702796492545]
  This study introduces a Robust Anomaly Detection dataset with free views, uneven illuminations, and blurry collections. RAD aims to identify foreign objects on working platforms as anomalies. We assess and analyze 11 state-of-the-art unsupervised and zero-shot methods on RAD.
  arXiv  Detail & Related papers  (2024-06-11T11:39:44Z)
• Overcoming Scene Context Constraints for Object Detection in wild using Defilters [3.038642416291856]
  High-level computer vision tasks such as object detection, recognition, and segmentation are particularly sensitive to image distortion. We propose an image defilter to rectify image distortion prior to object detection. This method enhances object detection accuracy, as models perform optimally when trained on non-distorted images.
  arXiv  Detail & Related papers  (2024-04-12T07:30:52Z)
• Visual Context-Aware Person Fall Detection [52.49277799455569]
  We present a segmentation pipeline to semi-automatically separate individuals and objects in images. Background objects such as beds, chairs, or wheelchairs can challenge fall detection systems, leading to false positive alarms. We demonstrate that object-specific contextual transformations during training effectively mitigate this challenge.
  arXiv  Detail & Related papers  (2024-04-11T19:06:36Z)
• Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems [80.62854148838359]
  Eye image segmentation is a critical step in eye tracking that has great influence over the final gaze estimate. We use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data. Our methods result in robust, improved performance when tackling the discrepancy between simulation and real-world data samples.
  arXiv  Detail & Related papers  (2024-03-23T22:32:06Z)
• An Ensemble Model for Distorted Images in Real Scenarios [0.0]
  In this paper, we apply the object detector YOLOv7 to detect distorted images from the CDCOCO dataset. Through carefully designed optimizations, our model achieves excellent performance on the CDCOCO test set. Our denoising detection model can denoise and repair distorted images, making the model useful in a variety of real-world scenarios and environments.
  arXiv  Detail & Related papers  (2023-09-26T15:12:55Z)
• Self-Supervised Graph Transformer for Deepfake Detection [1.8133635752982105]
  Deepfake detection methods have shown promising results in recognizing forgeries within a given dataset. Deepfake detection system must remain impartial to forgery types, appearance, and quality for guaranteed generalizable detection performance. This study introduces a deepfake detection framework, leveraging a self-supervised pre-training model that delivers exceptional generalization ability.
  arXiv  Detail & Related papers  (2023-07-27T17:22:41Z)
• Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection [77.3530907443279]
  We propose a novel self-supervised framework to detect objects in degraded low resolution images. Our methods has achieved superior performance compared with existing methods when facing variant degradation situations.
  arXiv  Detail & Related papers  (2022-08-05T09:36:13Z)
• ObjectFormer for Image Manipulation Detection and Localization [118.89882740099137]
  We propose ObjectFormer to detect and localize image manipulations. We extract high-frequency features of the images and combine them with RGB features as multimodal patch embeddings. We conduct extensive experiments on various datasets and the results verify the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-03-28T12:27:34Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)
• Contemplating real-world object classification [53.10151901863263]
  We reanalyze the ObjectNet dataset recently proposed by Barbu et al. containing objects in daily life situations. We find that applying deep models to the isolated objects, rather than the entire scene as is done in the original paper, results in around 20-30% performance improvement.
  arXiv  Detail & Related papers  (2021-03-08T23:29:59Z)

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.