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

TL;DR

This paper introduces a deep learning framework inspired by human visual cues to improve object detection accuracy and efficiency in low visibility conditions like fog and haze, using multi-tiered detection and dehazing techniques.

Contribution

It presents a novel multi-tiered detection strategy that incorporates human visual principles, significantly enhancing detection performance in adverse weather conditions.

Findings

Improved detection accuracy on Foggy Cityscapes and RESIDE-beta datasets.

Enhanced computational efficiency compared to existing methods.

Set new benchmarks in low visibility object detection performance.

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.