Object Detection Method Based on Polarimetric Features and PFOD-Net Under Adverse Weather Conditions
Source abstract
Abstract
To address insufficient real-time detection accuracy of standard YOLO models under adverse weather, we propose PFOD-Net, a multi-scale detector based on polarimetric features and an improved YOLOv8n. Compared with traditional intensity imaging, polarimetric imaging extracts optical information from target scenes more effectively; when coupled with an appropriate feature selection mechanism, it significantly enhances detection and recognition performance in complex environments. Experimental results on the Polar LITIS dataset demonstrate that PFOD-Net significantly outperforms YOLOv8 in both accuracy and speed: mAP@0.5 (mean Average Precision) increased from 33.5% to 81.7%—an increase of 48.2 percentage points. Notably, the detection performance of PFOD-Net is substantially reinforced in complex conditions like fog and haze. By innovatively integrating polarimetric information, lightweight architectures, and the fusion of max-pooling and average-pooling, this method provides an effective solution for object detection and recognition in adverse weather.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Optical Polarization and Ellipsometry · Synthetic Aperture Radar (SAR) Applications and Techniques · Remote-Sensing Image Classification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, No. 17 Xinxi Avenue, New Industrial Park, Xi’an High-Tech Zone, Xi’an 710049, China
University of Chinese Academy of Sciences, Yanqihu East Road, Huairou District, Beijing 100049, China
Funding
National Science and Technology Major Project
References (30)
Show source-provided references
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