Perceptual Piercing: Human Visual Cue-based Object Detection in Low Visibility Conditions

• URL: http://arxiv.org/abs/2410.01225v3
• Date: Fri, 07 Feb 2025 20:12:39 GMT
• Title: Perceptual Piercing: Human Visual Cue-based Object Detection in Low Visibility Conditions
• Authors: Ashutosh Kumar,
• Abstract summary: This study proposes a novel deep learning framework inspired by atmospheric scattering and human visual cortex mechanisms to enhance object detection under poor visibility scenarios such as fog, smoke, and haze. The objective is to enhance the precision and reliability of detection systems under adverse environmental conditions.
• Score: 2.0409124291940826
• License:
• Abstract: This study proposes a novel deep learning framework inspired by atmospheric scattering and human visual cortex mechanisms to enhance object detection under poor visibility scenarios such as fog, smoke, and haze. These conditions pose significant challenges for object recognition, impacting various sectors, including autonomous driving, aviation management, and security systems. The objective is to enhance the precision and reliability of detection systems under adverse environmental conditions. The research investigates the integration of human-like visual cues, particularly focusing on selective attention and environmental adaptability, to ascertain their impact on object detection's computational efficiency and accuracy. This paper proposes a multi-tiered strategy that integrates an initial quick detection process, followed by targeted region-specific dehazing, and concludes with an in-depth detection phase. The approach is validated using the Foggy Cityscapes, RESIDE-beta (OTS and RTTS) datasets and is anticipated to set new performance standards in detection accuracy while significantly optimizing computational efficiency. The findings offer a viable solution for enhancing object detection in poor visibility and contribute to the broader understanding of integrating human visual principles into deep learning algorithms for intricate visual recognition challenges.

Related papers

• Object Detection and Tracking [0.0]
  Project aims to integrate a modern technique for object detection with the aim of achieving high accuracy with real-time performance. In this research, we solve the end-to-end object detection problem entirely using deep learning techniques.
  arXiv  Detail & Related papers  (2025-02-14T17:13:52Z)
• From Fog to Failure: How Dehazing Can Harm Clear Image Object Detection [2.76348549160803]
  This study explores the challenges of integrating human visual cue-based dehazing into object detection. We propose a multi-stage framework where a lightweight detector identifies regions of interest, which are then enhanced via spatial attention-based dehazing. Though effective in foggy conditions, this approach unexpectedly degrades the performance on clear images.
  arXiv  Detail & Related papers  (2025-02-04T05:24:44Z)
• Integrity Monitoring of 3D Object Detection in Automated Driving Systems using Raw Activation Patterns and Spatial Filtering [12.384452095533396]
  The deep neural network (DNN) models are widely used for object detection in automated driving systems (ADS) Yet, such models are prone to errors which can have serious safety implications. Introspection and self-assessment models that aim to detect such errors are therefore of paramount importance for the safe deployment of ADS.
  arXiv  Detail & Related papers  (2024-05-13T10:03:03Z)
• OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
  Trajectory prediction is fundamental in computer vision and autonomous driving. Existing approaches in this field often assume precise and complete observational data. We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
  arXiv  Detail & Related papers  (2024-04-02T18:30:29Z)
• Collaborative Knowledge Infusion for Low-resource Stance Detection [83.88515573352795]
  Target-related knowledge is often needed to assist stance detection models. We propose a collaborative knowledge infusion approach for low-resource stance detection tasks.
  arXiv  Detail & Related papers  (2024-03-28T08:32:14Z)
• Object Detectors in the Open Environment: Challenges, Solutions, and Outlook [95.3317059617271]
  The dynamic and intricate nature of the open environment poses novel and formidable challenges to object detectors. This paper aims to conduct a comprehensive review and analysis of object detectors in open environments. We propose a framework that includes four quadrants (i.e., out-of-domain, out-of-category, robust learning, and incremental learning) based on the dimensions of the data / target changes.
  arXiv  Detail & Related papers  (2024-03-24T19:32:39Z)
• Exploring Hyperspectral Anomaly Detection with Human Vision: A Small Target Aware Detector [20.845503528474328]
  Hyperspectral anomaly detection (HAD) aims to localize pixel points whose spectral features differ from the background. Existing HAD methods aim to objectively detect and distinguish background and anomalous spectra. In this paper, we analyze hyperspectral image (HSI) features under human visual perception. We propose a small target aware detector (STAD), which introduces saliency maps to capture HSI features closer to human visual perception.
  arXiv  Detail & Related papers  (2024-01-02T08:28:38Z)
• 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)
• A Comprehensive Study on Object Detection Techniques in Unconstrained Environments [0.0]
  Object detection is a crucial task in computer vision that aims to identify and localize objects in images or videos. The recent advancements in deep learning and Convolutional Neural Networks (CNNs) have significantly improved the performance of object detection techniques. This paper presents a comprehensive study of object detection techniques in unconstrained environments, including various challenges, datasets, and state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-04-11T15:45:03Z)
• Adversarially-Aware Robust Object Detector [85.10894272034135]
  We propose a Robust Detector (RobustDet) based on adversarially-aware convolution to disentangle gradients for model learning on clean and adversarial images. Our model effectively disentangles gradients and significantly enhances the detection robustness with maintaining the detection ability on clean images.
  arXiv  Detail & Related papers  (2022-07-13T13:59:59Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)

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.