WDDR-YOLO: a Driver Distraction Detection Algorithm.
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| Title: | WDDR-YOLO: a Driver Distraction Detection Algorithm. |
|---|---|
| Authors: | Lu, Yan1 wylylu@163.com, Xing, Zhongyu1 xingzhongyu@gxjcxy.edu.cn, Li, Meng1 1052590135@qq.com, Liu, Guoqiang2 15693881490@163.com |
| Source: | Engineering Letters. May2026, Vol. 34 Issue 5, p1744-1756. 13p. |
| Subjects: | Distracted driving, Discrete wavelet transforms, Object recognition (Computer vision) |
| Abstract: | To address the challenge that deep visual models struggle to attain a well-tuned equilibrium between detection accuracy and inference efficiency in resource-limited operational contexts, this study proposes a frequency-domain dynamic reparameterization model, WDDR-YOLO. This method takes YOLOv11 as the baseline, with core innovations as follows: 1) Haar Wavelet Decomposition (HWD) is adopted to replace spatial downsampling, enabling the reorganization of features into the frequency-domain channel dimension; 2) A frequency-domain energy-aware Reparameterization Convolutional Structure (RCS) is designed, which extracts frequency-domain features through multiple branches during training and dynamically fuses them into a single convolution kernel during inference; 3) A collaborative mechanism between Multi-Local Channel Attention (MLCA) and Dynamic Upsampling (DySample) is constructed, enhancing the ability to retain high-frequency details through frequency-domain weighting and offset constraints. Experimental results for the driver distraction detection task demonstrate that WDDR-YOLO attains an mAP@50 of 87.0% on the State-Farm dataset, a metric that outperforms the baseline by a margin of 3.9 percentage points, alongside a 17.6% cut in model parameters and a rise in inference speed to 81.4 FPS. It further attains a value of 83.5% for mAP@50 on the AUC DDD dataset, which validates the model's generalization capability across heterogeneous scenarios. The proposed frequency-domain dynamic reparameterization paradigm provides an extensible new approach for the design of lightweight visual perception models. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: WDDR-YOLO: a Driver Distraction Detection Algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lu%2C+Yan%22">Lu, Yan</searchLink><relatesTo>1</relatesTo><i> wylylu@163.com</i><br /><searchLink fieldCode="AR" term="%22Xing%2C+Zhongyu%22">Xing, Zhongyu</searchLink><relatesTo>1</relatesTo><i> xingzhongyu@gxjcxy.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Meng%22">Li, Meng</searchLink><relatesTo>1</relatesTo><i> 1052590135@qq.com</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Guoqiang%22">Liu, Guoqiang</searchLink><relatesTo>2</relatesTo><i> 15693881490@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. May2026, Vol. 34 Issue 5, p1744-1756. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Distracted+driving%22">Distracted driving</searchLink><br /><searchLink fieldCode="DE" term="%22Discrete+wavelet+transforms%22">Discrete wavelet transforms</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To address the challenge that deep visual models struggle to attain a well-tuned equilibrium between detection accuracy and inference efficiency in resource-limited operational contexts, this study proposes a frequency-domain dynamic reparameterization model, WDDR-YOLO. This method takes YOLOv11 as the baseline, with core innovations as follows: 1) Haar Wavelet Decomposition (HWD) is adopted to replace spatial downsampling, enabling the reorganization of features into the frequency-domain channel dimension; 2) A frequency-domain energy-aware Reparameterization Convolutional Structure (RCS) is designed, which extracts frequency-domain features through multiple branches during training and dynamically fuses them into a single convolution kernel during inference; 3) A collaborative mechanism between Multi-Local Channel Attention (MLCA) and Dynamic Upsampling (DySample) is constructed, enhancing the ability to retain high-frequency details through frequency-domain weighting and offset constraints. Experimental results for the driver distraction detection task demonstrate that WDDR-YOLO attains an mAP@50 of 87.0% on the State-Farm dataset, a metric that outperforms the baseline by a margin of 3.9 percentage points, alongside a 17.6% cut in model parameters and a rise in inference speed to 81.4 FPS. It further attains a value of 83.5% for mAP@50 on the AUC DDD dataset, which validates the model's generalization capability across heterogeneous scenarios. The proposed frequency-domain dynamic reparameterization paradigm provides an extensible new approach for the design of lightweight visual perception models. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1744 Subjects: – SubjectFull: Distracted driving Type: general – SubjectFull: Discrete wavelet transforms Type: general – SubjectFull: Object recognition (Computer vision) Type: general Titles: – TitleFull: WDDR-YOLO: a Driver Distraction Detection Algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lu, Yan – PersonEntity: Name: NameFull: Xing, Zhongyu – PersonEntity: Name: NameFull: Li, Meng – PersonEntity: Name: NameFull: Liu, Guoqiang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1816093X Numbering: – Type: volume Value: 34 – Type: issue Value: 5 Titles: – TitleFull: Engineering Letters Type: main |
| ResultId | 1 |
