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

arXiv: https://arxiv.org/abs/2410.01225 · HTML: https://mlsanity.com/paper/2410.01225/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Benchmarking Single Image Dehazing and Beyond](https://mlsanity.com/paper/1712.04143/) (2017)
- [Semantic Foggy Scene Understanding with Synthetic Data](https://mlsanity.com/paper/1708.07819/) (2017)
- [A Study and Comparison of Human and Deep Learning Recognition Performance Under Visual Distortions](https://mlsanity.com/paper/1705.02498/) (2017)
- [UG2+ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments](https://mlsanity.com/paper/1904.04474/) (2019)
- [Comparing object recognition in humans and deep convolutional neural networks -- An eye tracking study](https://mlsanity.com/paper/2108.00107/) (2021)
- [Color-wise Attention Network for Low-light Image Enhancement](https://mlsanity.com/paper/1911.08681/) (2019)
- [Visual-Thermal Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments](https://mlsanity.com/paper/1903.01656/) (2019)
- [Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems](https://mlsanity.com/paper/2002.03472/) (2020)
- [Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements](https://mlsanity.com/paper/2206.10587/) (2022)
- [Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models](https://mlsanity.com/paper/2311.16475/) (2023)

## Abstract (arXiv)

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.