Polarization of road target detection under complex weather conditions
Abstract Polarization imaging technology can be applied to unveil the interaction between light and matter by harnessing the transverse vector wave attributes of light, thus to accentuate target characteristics amidst complex weather conditions. This technology has the potential to be widely used in...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-80830-3 |
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| author | Feng Huang Junlong Zheng Xiancai Liu Ying Shen Jinsheng Chen |
| author_facet | Feng Huang Junlong Zheng Xiancai Liu Ying Shen Jinsheng Chen |
| author_sort | Feng Huang |
| collection | DOAJ |
| description | Abstract Polarization imaging technology can be applied to unveil the interaction between light and matter by harnessing the transverse vector wave attributes of light, thus to accentuate target characteristics amidst complex weather conditions. This technology has the potential to be widely used in road target detection. However, polarization detection is significantly affected by illumination and detection angles, as well as the considerable variation in the scale of road targets. The optimal polarization parameters should be adaptively adjusted to weather conditions, angles and target features, whereas most existing research employs handcrafted polarization parameters without considering actual complex detection requirements, which are unable to adaptively adjust the polarization feature enhancement methods. In this paper, we propose a road target detection algorithm based on an end-to-end adaptive polarization coding method, named YOLO-Polarization of Road Target Detection (YOLO-PRTD). To enhance the polarized features of targets under complex weather conditions, an Adaptive Polarization Coding Module (APCM) is designed. This module integrates channel-wise global self-attention and small kernel convolution to adaptively adjust the polarization enhancement method using dynamically extracted global and local polarization feature information. A multi-scale detection network is also designed to fully extract and fuse multi-scale feature information from receptive fields, channels, and spaces in different dimensions. Additionally, a dataset of Polarized Images of Road Targets in Complex Weather conditions (PIRT-CW) is proposed for training and evaluation. Experimental results on the PIRT-CW show that the YOLO-PRTD algorithm achieves a mAP0.5 of 89.83%, reducing the error rate by 15.54% compared to the baseline network YOLOX. |
| format | Article |
| id | doaj-art-a63a06a3f3fa44afafab01a97f8ccc64 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a63a06a3f3fa44afafab01a97f8ccc642025-08-20T02:30:56ZengNature PortfolioScientific Reports2045-23222024-12-0114111810.1038/s41598-024-80830-3Polarization of road target detection under complex weather conditionsFeng Huang0Junlong Zheng1Xiancai Liu2Ying Shen3Jinsheng Chen4College of Mechanical Engineering and Automation, Fuzhou UniversityCollege of Mechanical Engineering and Automation, Fuzhou UniversityCollege of Mechanical Engineering and Automation, Fuzhou UniversityCollege of Mechanical Engineering and Automation, Fuzhou UniversityFujian Communications Planning & Design Institute CO., LTDAbstract Polarization imaging technology can be applied to unveil the interaction between light and matter by harnessing the transverse vector wave attributes of light, thus to accentuate target characteristics amidst complex weather conditions. This technology has the potential to be widely used in road target detection. However, polarization detection is significantly affected by illumination and detection angles, as well as the considerable variation in the scale of road targets. The optimal polarization parameters should be adaptively adjusted to weather conditions, angles and target features, whereas most existing research employs handcrafted polarization parameters without considering actual complex detection requirements, which are unable to adaptively adjust the polarization feature enhancement methods. In this paper, we propose a road target detection algorithm based on an end-to-end adaptive polarization coding method, named YOLO-Polarization of Road Target Detection (YOLO-PRTD). To enhance the polarized features of targets under complex weather conditions, an Adaptive Polarization Coding Module (APCM) is designed. This module integrates channel-wise global self-attention and small kernel convolution to adaptively adjust the polarization enhancement method using dynamically extracted global and local polarization feature information. A multi-scale detection network is also designed to fully extract and fuse multi-scale feature information from receptive fields, channels, and spaces in different dimensions. Additionally, a dataset of Polarized Images of Road Targets in Complex Weather conditions (PIRT-CW) is proposed for training and evaluation. Experimental results on the PIRT-CW show that the YOLO-PRTD algorithm achieves a mAP0.5 of 89.83%, reducing the error rate by 15.54% compared to the baseline network YOLOX.https://doi.org/10.1038/s41598-024-80830-3Polarization feature enhancementMulti-scaleComplex weather conditionsRoad target detection |
| spellingShingle | Feng Huang Junlong Zheng Xiancai Liu Ying Shen Jinsheng Chen Polarization of road target detection under complex weather conditions Scientific Reports Polarization feature enhancement Multi-scale Complex weather conditions Road target detection |
| title | Polarization of road target detection under complex weather conditions |
| title_full | Polarization of road target detection under complex weather conditions |
| title_fullStr | Polarization of road target detection under complex weather conditions |
| title_full_unstemmed | Polarization of road target detection under complex weather conditions |
| title_short | Polarization of road target detection under complex weather conditions |
| title_sort | polarization of road target detection under complex weather conditions |
| topic | Polarization feature enhancement Multi-scale Complex weather conditions Road target detection |
| url | https://doi.org/10.1038/s41598-024-80830-3 |
| work_keys_str_mv | AT fenghuang polarizationofroadtargetdetectionundercomplexweatherconditions AT junlongzheng polarizationofroadtargetdetectionundercomplexweatherconditions AT xiancailiu polarizationofroadtargetdetectionundercomplexweatherconditions AT yingshen polarizationofroadtargetdetectionundercomplexweatherconditions AT jinshengchen polarizationofroadtargetdetectionundercomplexweatherconditions |