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A lightweight detection network named HDSL⁃YOLO is designed to avoid the hardware resource constraints of in⁃vehicle systems and improve the deployability and accuracy rate of distracted driving detection models. On the basis of the YOLOv8n, the model is improved by incorporating multiple optimization strategies, including the following four key aspects: firstly, the HGNetV2 lightweight backbone network is introduced to effectively reduce parameter size while significantly improving detection speed and operational efficiency; secondly, the dynamic upsampling module (Dysample) is integrated to enhance feature representation, particularly in multi⁃scale object extraction; additionally, the SimAM (simple attention module) is incorporated to further strengthen the model′s perception and recognition capabilities for small objects; and finally, a lightweight detection head is adopted to further streamline parameters. Experiments demonstrate that the mAP@0.5 and mAP@0.5:0.95 of the HDSL⁃YOLO algorithm is improved by 92.4% and 55.4%, respectively. In comparison with the original YOLOv8n algorithm, the improved algorithm realizes not only an improved detection accuracy rate, but also a further lightweighting, so it achieves dual optimization. Deploying the HDSL⁃YOLO on the embedded platform Jetson Nano confirms shorter response times, which validates the effectiveness of the proposed improvements.
[1] Shi Yulin, Cheng Jintao, Chen Xingming, et al. Mfdd: Multi⁃ scale attention fatigue and distracted driving detector based on facial features [J]. Journal of Real ⁃ time Image Processing, 2024, 21(5): 170.
[2] Gjorgjievski M, Petrisor B, Sprague S, et al. The ROADS project: Road observational assessment of driving distractions [J]. Journal of Safety Research, 2025, 92: 91⁃97.
[3] Badue C, Guidolini R, Carneiro R V, et al. Self⁃driving cars: A survey [J]. Expert Systems with Aapplications, 2021, 165: 113816.
[4] Zhou Keke, Zheng Guoqiang, Zhai Huihui, et al. An improved distraction behavior detection algorithm based on YOLOv5 [J]. Computers, Materials & Continua, 2024, 81(2): 2571⁃2585.
[5] 朱周华，侯智杰，田成源，等.基于改进YOLOv8⁃pose的分心驾驶检测与识别[J].电子测量技术，2024，47（15）：135⁃143.
[6] 沈澍，刘秉松，朱浩，等.结合多视图学习的分心驾驶行为识别方法研究[J].小型微型计算机系统，2025，46（7）：1638⁃1644.
[7] Zhao Zuopeng, Zhao Haihan, Ye Chen, et al. FPN⁃D⁃based driver smoking behavior detection method [J]. IETE Journal of Research, 2023, 69(8): 5497⁃5506.
[8] 欧阳壮，朱天军，文浩.基于深度学习的驾驶员分心驾驶行为预警算法[J].机电工程技术，2023，52（12）：27⁃30.
[9] Debsi A, Ling Guo, Al⁃mahbashi M, et al. Driver distraction and fatigue detection in images using ME⁃YOLOv8 algorithm [J]. IET Intelligent Transport Systems, 2024, 18(10): 1910⁃1930.
[10] Du Yingjie, Liu Xiaofeng, Yi Yuwei, et al. Incorporating bidirectional feature pyramid network and lightweight network: A YOLOv5⁃GBC distracted driving behavior detection model [J]. Neural Computing and Applications, 2024, 36(17): 9903 ⁃ 9917.
[11] Li Ruijie, Yu Changdong, Qin Xiangrong, et al. YOLO⁃SGC: A dangerous driving behavior detection method with multiscale spatial ⁃ channel feature aggregation [J]. IEEE Sensors Journal, 2024, 24(21): 36044⁃36056.
[12] Qin Long, Shi Yi, He Yahui, et al. ID⁃YOLO: Real⁃time salient object detection based on the driver′s fixation region [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9): 15898⁃15908.
[13] Mazin H, Hayder R. Integrated multiscale domain adaptive YOLO [J]. IEEE Transactions on Image Processing, 2023, 32: 1857⁃1867.
[14] He Wenbin, Yang Shuai, Liu Zhaohui, et al. Risk identification of distracted driving behavior based on transfer learning [C]// Proceedings of 2024 International Conference on Vehicle Safety and Intelligent Transportation. Guangzhou, China: [s.n.], 2024: 49310.
[15] Du Yingjie, Liu Xiaofeng, Yi Yuwei, et al. Optimizing road safety: Advancements in lightweight YOLOv8 models and ghostC2f design for real⁃time distracted driving detection [J]. Sensors, 2023, 23(21): 18844.
[16] 沈骞，张磊，张宇翔，等.基于改进YOLOv8n的轻量化分心驾驶行为检测方法[J].电子测量技术，2024，47（24）：65⁃75.
[17] 朱玉华，龚晓腾，吴宁.基于改进YOLOv8n的轻量化分心驾驶检测算法[J].汽车工程师，2024（10）：37⁃43.
[18] Zhang Lei, Zheng Jiachun, Li Chaopeng, et al. CCDN⁃DETR: A detection transformer based on constrained contrast denoising for multi ⁃ class synthetic aperture radar object detection [J]. Sensors, 2024, 24(6): 61793.
[19] Vorugunti C S, Pulabaigari V, Gorthi R K S S, et al. OSVFuseNet: Online signature verification by feature fusion and depth⁃wise separable convolution based deep learning [J]. Neurocomputing, 2020, 409: 157⁃172.
[20] Liu Wenze, Lu Hao, Fu Hongtao, et al. Learning to upsample by learning to sample [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision. Paris, France: IEEE, 2023: 6004⁃6014.
[21] Yang Lingxiao, Zhang Ruyuan, Li Lida, et al. SimAM: A simple, parameter ⁃ free attention module for convolutional neural networks [C]// International Conference on Machine Learning. [S.l.]: PMLR, 2021: 11853⁃11864.
Basic Information:
DOI：10.16652/j.issn.1004⁃373x.2026.09.026
Citation Information:
[1]Zhong Yalu12, Kong Yanqi12, Lin Chen12 ,et al.Research on distracted driving detection method based on lightweight networks[J].Modern Electronic Technique,2026(9):178-184.DOI:10.16652/j.issn.1004⁃373x.2026.09.026.
Fund Information:
福建省自然科学基金项目（2023H0022，2024J01418）; 福建农林大学科技创新专项基金项目（KFB23165A）