{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:29:15Z","timestamp":1771025355762,"version":"3.50.1"},"reference-count":18,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T00:00:00Z","timestamp":1739491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100015321","name":"Universidade de Tr\u00e1s-os-Montes e Alto Douro","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100015321","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Riding a motorcycle involves risks that can be minimized through advanced sensing and response systems to assist the rider. The use of camera-collected images to monitor road conditions can aid in the development of tools designed to enhance rider safety and prevent accidents. This paper proposes a method for developing deep learning models designed to operate efficiently on embedded systems like the Raspberry Pi, facilitating real-time decisions that consider the road condition. Our research tests and compares several state-of-the-art convolutional neural network architectures, including EfficientNet and Inception, to determine which offers the best balance between inference time and accuracy. Specifically, we measured top-1 accuracy and inference time on a Raspberry Pi, identifying EfficientNetV2 as the most suitable model due to its optimal trade-off between performance and computational demand. The model's top-1 accuracy significantly outperformed other models while maintaining competitive inference speeds, making it ideal for real-time applications in traffic-dense urban settings.<\/jats:p>","DOI":"10.3389\/frai.2025.1520557","type":"journal-article","created":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T06:59:33Z","timestamp":1739516373000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Advanced driving assistance integration in electric motorcycles: road surface classification with a focus on gravel detection using deep learning"],"prefix":"10.3389","volume":"8","author":[{"given":"Ranan","family":"Venancio","sequence":"first","affiliation":[]},{"given":"Vitor","family":"Filipe","sequence":"additional","affiliation":[]},{"given":"Adelaide","family":"Cerveira","sequence":"additional","affiliation":[]},{"given":"Lio","family":"Gon\u00e7alves","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,2,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"4705","DOI":"10.3390\/app14114705","article-title":"A review of deep learning advancements in road analysis for autonomous driving","volume":"14","author":"Botezatu","year":"2024","journal-title":"Appl. Sci"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2010.11929","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv"},{"key":"B3","first-page":"770","article-title":"\u201cDeep residual learning for image recognition,\u201d","volume-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"He","year":"2015"},{"key":"B4","first-page":"1314","article-title":"\u201cSearching for MobileNetV3,\u201d","volume-title":"Proceedings of the IEEE\/CVF international conference on computer vision","author":"Howard","year":"2019"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1704.04861","article-title":"Mobilenets: efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017","journal-title":"arXiv"},{"key":"B6","doi-asserted-by":"crossref","first-page":"7132","DOI":"10.1109\/CVPR.2018.00745","article-title":"\u201cSqueeze-and-excitation networks,\u201d","volume-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Hu","year":"2018"},{"key":"B7","article-title":"\u201cImageNet classification with deep convolutional neural networks,\u201d","author":"Krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems, Volume 25"},{"key":"B8","doi-asserted-by":"publisher","first-page":"3233","DOI":"10.3390\/s21093233","article-title":"Intelligent tire sensor-based real-time road surface classification using an artificial neural network","volume":"21","author":"Lee","year":"2021","journal-title":"Sensors"},{"key":"B9","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/ICCV.2017.324","article-title":"Focal loss for dense object detection","volume":"42","author":"Lin","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"B10","doi-asserted-by":"crossref","first-page":"9992","DOI":"10.1109\/ICCV48922.2021.00986","article-title":"\u201cSwin transformer: Hierarchical vision transformer using shifted windows,\u201d","volume-title":"2021 IEEE\/CVF International Conference on Computer Vision (ICCV)","author":"Liu","year":"2021"},{"key":"B11","doi-asserted-by":"crossref","first-page":"11966","DOI":"10.1109\/CVPR52688.2022.01167","article-title":"\u201cA ConvNet for the 2020s,\u201d","volume-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Liu","year":"2022"},{"key":"B12","doi-asserted-by":"crossref","first-page":"4510","DOI":"10.1109\/CVPR.2018.00474","article-title":"\u201cMobilenetv2: inverted residuals and linear bottlenecks,\u201d","volume-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Sandler","year":"2018"},{"key":"B13","first-page":"2818","article-title":"\u201cRethinking the inception architecture for computer vision,\u201d","volume-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Szegedy","year":"2015"},{"key":"B14","first-page":"2815","article-title":"\u201cMnasNet: platform-aware neural architecture search for mobile,\u201d","volume-title":"2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Tan","year":"2018"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.485550\/arXiv.1905.11946","article-title":"EfficientNet: rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019","journal-title":"arXiv"},{"key":"B16","unstructured":"\u201cEfficientNetV2: smaller models and faster training,\u201d\n          \n          1009\n          10106\n          \n            \n              Tan\n              M.\n            \n            \n              Le\n              Q. V.\n            \n          \n          Proceedings of the 38th International Conference on Machine Learning\n          \n          2021"},{"key":"B17","unstructured":"\u201cAttention is all you need,\u201d\n          \n          \n            \n              Vaswani\n              A.\n            \n            \n              Shazeer\n              N. M.\n            \n            \n              Parmar\n              N.\n            \n            \n              Uszkoreit\n              J.\n            \n            \n              Jones\n              L.\n            \n            \n              Gomez\n              A. N.\n            \n          \n          Long Beach, CA\n          Neural Information Processing Systems Foundation, Inc.\n          Advances in Neural Information Processing Systems, Vol. 30\n          \n          2017"},{"key":"B18","doi-asserted-by":"publisher","first-page":"108483","DOI":"10.1016\/j.dib.2022.108483","article-title":"A road surface image dataset with detailed annotations for driving assistance applications","volume":"43","author":"Zhao","year":"2022","journal-title":"Data Brief"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1520557\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T06:59:42Z","timestamp":1739516382000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1520557\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,14]]},"references-count":18,"alternative-id":["10.3389\/frai.2025.1520557"],"URL":"https:\/\/doi.org\/10.3389\/frai.2025.1520557","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,14]]},"article-number":"1520557"}}