{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:35:45Z","timestamp":1787319345658,"version":"build-2736575974"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T00:00:00Z","timestamp":1769299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangzhou Development Zone Science and Technology Project","award":["2023GH02"],"award-info":[{"award-number":["2023GH02"]}]},{"DOI":"10.13039\/501100004733","name":"University of Macau","doi-asserted-by":"crossref","award":["MYRG2022-00271-FST"],"award-info":[{"award-number":["MYRG2022-00271-FST"]}],"id":[{"id":"10.13039\/501100004733","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science and Technology Development Fund of Macau","award":["0032\/2022\/A"],"award-info":[{"award-number":["0032\/2022\/A"]}]},{"name":"Science and Technology Development Fund of Macau","award":["0019\/2025\/RIB1"],"award-info":[{"award-number":["0019\/2025\/RIB1"]}]},{"name":"Science and Technology Development Fund of Macau","award":["0024\/2025\/AIJ"],"award-info":[{"award-number":["0024\/2025\/AIJ"]}]},{"name":"ZUMRI-DeepFuture Technology Joint Lab","award":["CP-104-2025 (2)"],"award-info":[{"award-number":["CP-104-2025 (2)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Ensuring sustained driver attention is critical for intelligent transportation safety systems; however, the performance of data-driven driver distraction detection models is often limited by the high cost of large-scale manual annotation. To address this challenge, this paper proposes an adaptive semi-supervised driver distraction detection framework based on teacher\u2013student learning and deformable pyramid feature fusion. The framework leverages a limited amount of labeled data together with abundant unlabeled samples to achieve robust and scalable distraction detection. An adaptive pseudo-label optimization strategy is introduced, incorporating category-aware pseudo-label thresholding, delayed pseudo-label scheduling, and a confidence-weighted pseudo-label loss to dynamically balance pseudo-label quality and training stability. To enhance fine-grained perception of subtle driver behaviors, a Deformable Pyramid Sparse Transformer (DPST) module is integrated into a lightweight YOLOv11 detector, enabling precise multi-scale feature alignment and efficient cross-scale semantic fusion. Furthermore, a teacher-guided feature consistency distillation mechanism is employed to promote semantic alignment between teacher and student models at the feature level, mitigating the adverse effects of noisy pseudo-labels. Extensive experiments conducted on the Roboflow Distracted Driving Dataset demonstrate that the proposed method outperforms representative fully supervised baselines in terms of mAP@0.5 and mAP@0.5:0.95 while maintaining a balanced trade-off between precision and recall. These results indicate that the proposed framework provides an effective and practical solution for real-world driver monitoring systems under limited annotation conditions.<\/jats:p>","DOI":"10.3390\/s26030803","type":"journal-article","created":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T16:13:48Z","timestamp":1769444028000},"page":"803","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deformable Pyramid Sparse Transformer for Semi-Supervised Driver Distraction Detection"],"prefix":"10.3390","volume":"26","author":[{"given":"Qiang","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhichao","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahui","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1848-7246","authenticated-orcid":false,"given":"Simon James","family":"Fong","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchu","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Macau University of Science and Technology, Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7974-9510","authenticated-orcid":false,"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6876-1795","authenticated-orcid":false,"given":"Weiwei","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"83138","DOI":"10.1109\/ACCESS.2022.3197146","article-title":"AB-DLM: An improved deep learning model based on attention mechanism and BiFPN for driver distraction behavior detection","volume":"10","author":"Li","year":"2022","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111932","DOI":"10.1016\/j.engappai.2025.111932","article-title":"An improved genetic algorithm based deep learning model with you only look once framework for driver distraction detection","volume":"160","author":"Rampavan","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"131170","DOI":"10.1016\/j.eswa.2026.131170","article-title":"Ddr-yolo: An efficient and accurate object detection algorithm for distracted driving behaviors","volume":"308","author":"Shen","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"ref_4","first-page":"1594","article-title":"FPT: Fine-grained detection of driver distraction based on the feature pyramid vision transformer","volume":"24","author":"Wang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"121066","DOI":"10.1016\/j.eswa.2023.121066","article-title":"Multimodal driver distraction detection using dual-channel network of CNN and Transformer","volume":"234","author":"Mou","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"140","DOI":"10.3390\/vehicles6010006","article-title":"Highly discriminative driver distraction detection method based on Swin transformer","volume":"6","author":"Zhang","year":"2024","journal-title":"Vehicles"},{"key":"ref_7","unstructured":"Jeong, J., Lee, S., Kim, J., and Kwak, N. (2019). Consistency-based semi-supervised learning for object detection. Adv. Neural Inf. Process. Syst., 32."},{"key":"ref_8","unstructured":"Sohn, K., Zhang, Z., Li, C.L., Zhang, H., Lee, C.Y., and Pfister, T. (2020). A simple semi-supervised learning framework for object detection. arXiv."},{"key":"ref_9","unstructured":"Liu, Y.-C., Ma, C.Y., He, Z., Kuo, C.W., Chen, K., Zhang, P., and Vajda, P. (2021). Unbiased teacher for semi-supervised object detection. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xu, M., Zhang, Z., Hu, H., Wang, J., Wang, L., Wei, F., Bai, X., and Liu, Z. (2021). End-to-end semi-supervised object detection with soft teacher. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV48922.2021.00305"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2151","DOI":"10.1109\/TCSVT.2023.3301854","article-title":"Semi-supervised few-shot object detection via adaptive pseudo labeling","volume":"34","author":"Tang","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_12","unstructured":"Wang, Y., Guo, J., Wang, J., Wu, C., Song, S., and Huang, G. (2020). Meta-semi: A meta-learning approach for semi-supervised learning. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, B., Chen, W., Yang, S., Xuan, Y., Song, J., Xie, D., Pu, S., Song, M., and Zhuang, Y. (2022). Label matching semi-supervised object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR52688.2022.01398"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8153","DOI":"10.1109\/TCSVT.2021.3098118","article-title":"Semi-supervised video object segmentation via learning object-aware global-local correspondence","volume":"32","author":"Fan","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Roy, R., Aich, U., Lyanova, A., Kaplun, D., and Sarkar, R. (2025). FSTS-Net: A Feature-mixing aided Semi-supervised Teacher-Student Network for Polyp Image Segmentation. 2025 21st International Symposium on Biomedical Image Processing and Analysis (SIPAIM), IEEE.","DOI":"10.1109\/SIPAIM67325.2025.11283149"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xie, Q., Luong, M.-T., Hovy, E., and Le, Q.V. (2020). Self-training with noisy student improves imagenet classification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"ref_17","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2020). Deformable detr: Deformable transformers for end-to-end object detection. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., and Shao, L. (2021). Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"LaBonte, T., Song, Y., Wang, X., Vineet, V., and Joshi, N. (2023). Scaling novel object detection with weakly supervised detection transformers. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, IEEE.","DOI":"10.1109\/WACV56688.2023.00017"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, J., Lin, X., Zhang, W., Tan, X., Gao, H., Wang, J., and Li, G. (2025). Towards Efficient Semi-Supervised Object Detection with Detection Transformer. IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE.","DOI":"10.1109\/TPAMI.2025.3642123"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez, A., Usamentiaga, R., Car\u00fas, J.L., and Casado, R. (2016). Driver distraction using visual-based sensors and algorithms. Sensors, 16.","DOI":"10.3390\/s16111805"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1109\/TITS.2007.895298","article-title":"Real-time detection of driver cognitive distraction using support vector machines","volume":"8","author":"Liang","year":"2007","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cojocaru, I., Popescu, P.S., and Mihaescu, M.C. (2022). Driver Behaviour Analysis based on Deep Learning Algorithms. RoCHI, RoCHI.","DOI":"10.37789\/rochi.2022.1.1.18"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.patrec.2017.12.023","article-title":"Detecting distraction of drivers using convolutional neural network","volume":"139","author":"Masood","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8823","DOI":"10.1109\/TITS.2021.3086411","article-title":"Driver distraction detection using octave-like convolutional neural network","volume":"23","author":"Li","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5783","DOI":"10.1109\/TITS.2021.3055545","article-title":"Fine-grained detection of driver distraction based on neural architecture search","volume":"22","author":"Chen","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105432","DOI":"10.1016\/j.aap.2020.105432","article-title":"Detection of driver manual distraction via image-based hand and ear recognition","volume":"137","author":"Li","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shen, Q., Zhang, L., Zhang, Y., Li, Y., Liu, S., and Xu, Y. (2024). Distracted driving behavior detection algorithm based on lightweight StarDL-YOLO. Electronics, 13.","DOI":"10.3390\/electronics13163216"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"112265","DOI":"10.1016\/j.engappai.2025.112265","article-title":"A multi-modal approach for detecting drivers\u2019 distraction using bio-signal and vision sensor fusion in driver monitoring systems","volume":"161","author":"Noh","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_31","first-page":"10","article-title":"RT-DETR Edge Deployment: Real-Time Detection Transformer for Distracted Driving Detection","volume":"16","author":"Aljahani","year":"2025","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Doshi, V. (2025). Anchor-ViT: Spatially-Focused Vision Transformer for Distracted Driving Detection. 2025 IEEE International Conference on Image Processing (ICIP), IEEE.","DOI":"10.1109\/ICIP55913.2025.11084655"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"125975","DOI":"10.1016\/j.eswa.2024.125975","article-title":"Driver Cognitive Distraction Detection based on eye movement behavior and integration of multi-view space-channel feature","volume":"266","author":"Qiao","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"107618","DOI":"10.1016\/j.engappai.2023.107618","article-title":"Driver distraction detection using semi-supervised lightweight vision transformer","volume":"129","author":"Mohammed","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1109\/TITS.2025.3625645","article-title":"Dsdformer: An innovative transformer-mamba framework for robust high-precision driver distraction identification","volume":"27","author":"Chen","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_36","unstructured":"Khanam, R., and Hussain, M. (2024). Yolov11: An overview of the key architectural enhancements. arXiv."},{"key":"ref_37","unstructured":"(2025, December 21). Ipylot Project. Distracted Driving Dataset (Roboflow Universe). Roboflow. Available online: https:\/\/universe.roboflow.com."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024, January 16\u201322). Detrs beat yolos on real-time object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_39","unstructured":"Tian, Y., Qixiang, Y., and Doermann, D. (2025). Yolov12: Attention-centric real-time object detectors. arXiv."},{"key":"ref_40","unstructured":"Lei, M., Li, S., Wu, Y., Hu, H., Zhou, Y., Zheng, X., and Gao, Y. (2025). YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception. arXiv."},{"key":"ref_41","unstructured":"Lv, W., Zhao, Y., Chang, Q., Huang, K., Wang, G., and Liu, Y. (2024). Rt-detrv2: Improved baseline with bag-of-freebies for real-time detection transformer. arXiv."},{"key":"ref_42","unstructured":"Hu, J., Bai, T., Wu, F., Peng, Z., and Zhang, Y. (2025). Pyramid Sparse Transformer: Enhancing Multi-Scale Feature Fusion with Dynamic Token Selection. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/3\/803\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T05:15:08Z","timestamp":1769836508000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/3\/803"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,25]]},"references-count":42,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["s26030803"],"URL":"https:\/\/doi.org\/10.3390\/s26030803","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,25]]}}}