{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:20:58Z","timestamp":1760149258846,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:00:00Z","timestamp":1689724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sanda University","award":["2021ZD06","2022BC088","2021ZD05"],"award-info":[{"award-number":["2021ZD06","2022BC088","2021ZD05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Road segmentation is beneficial to build a vision-controllable mission-oriented self-driving bot, e.g., the Self-Driving Sweeping Bot, or SDSB, for working in restricted areas. Using road segmentation, the bot itself and physical facilities may be protected and the sweeping efficiency of the SDSB promoted. However, roads in the real world are generally exposed to intricate noise conditions as a result of changing weather and climate effects; these include sunshine spots, shadowing caused by trees or physical facilities, traffic obstacles and signs, and cracks or sealing signs resulting from long-term road usage, as well as different types of road materials, such as cement or asphalt; all of these factors greatly influence the effectiveness of road segmentation. In this work, we investigate the extension of Primordial U-Net by the proposed EnRDeA U-Net, which uses an input channel applying a Residual U-Net block as an encoder and an attention gate in the output channel as a decoder, to validate a dataset of intricate road noises. In addition, we carry out a detailed analysis of the nets\u2019 features and segmentation performance to validate the intricate noises dataset on three U-Net extensions, i.e., the Primordial U-Net, Residual U-Net, and EnRDeA U-Net. Finally, the nets\u2019 structures, parameters, training losses, performance indexes, etc., are presented and discussed in the experimental results.<\/jats:p>","DOI":"10.3390\/e25071085","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T21:20:14Z","timestamp":1689801614000},"page":"1085","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["EnRDeA U-Net Deep Learning of Semantic Segmentation on Intricate Noise Roads"],"prefix":"10.3390","volume":"25","author":[{"given":"Xiaodong","family":"Yu","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Sanda University, No. 2727 Jinhai Road, Shanghai Pudong District, Shanghai 201209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ta-Wen","family":"Kuan","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Sanda University, No. 2727 Jinhai Road, Shanghai Pudong District, Shanghai 201209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shih-Pang","family":"Tseng","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Sanda University, No. 2727 Jinhai Road, Shanghai Pudong District, Shanghai 201209, China"},{"name":"School of Software and Big Data, Changzhou College of Information Technology, Changzhou 213164, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Sanda University, No. 2727 Jinhai Road, Shanghai Pudong District, Shanghai 201209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Chen","sequence":"additional","affiliation":[{"name":"Jiangsu Zero-Carbon Energy-Saving and Environmental Protection Technology, Yangzhou 225000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jhing-Fa","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Sanda University, No. 2727 Jinhai Road, Shanghai Pudong District, Shanghai 201209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhang","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Sanda University, No. 2727 Jinhai Road, Shanghai Pudong District, Shanghai 201209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tuoli","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Sanda University, No. 2727 Jinhai Road, Shanghai Pudong District, Shanghai 201209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,19]]},"reference":[{"key":"ref_1","unstructured":"Katiyar, S., Ibraheem, N., and Ansari, A.Q. (2015, January 8\u201312). Ant colony optimization: A tutorial review. Proceedings of the 10th IET International Conference on Advances in Power System Control, Operation and Management (APSCOM 2015), Hong Kong, China."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kuan, T.W., Chen, S., Luo, S.N., Chen, Y., Wang, J.F., and Wang, C. (2021, January 16\u201317). Perspective on SDSB Human Visual Knowledge and Intelligence for Happiness Campus. Proceedings of the 2021 9th International Conference on Orange Technology (ICOT), Tainan, Taiwan.","DOI":"10.1109\/ICOT54518.2021.9680637"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kuan, T.W., Xiao, G., Wang, Y., Chen, S., Chen, Y., and Wang, J.-F. (2022, January 10\u201311). Human Knowledge and Visual Intelligence on SDXtensionB. Proceedings of the 2022 10th International Conference on Orange Technology (ICOT), Shanghai, China.","DOI":"10.1109\/ICOT56925.2022.10008159"},{"key":"ref_4","unstructured":"Medina, M. (2007). The World\u2019s Scavengers: Salvaging for Sustainable Consumption and Production, Rowman Altamira."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yu, X., Kuan, T.W., Zhang, Y., and Yan, T. (2022, January 10\u201311). YOLO v5 for SDSB Distant Tiny Object Detection. Proceedings of the 2022 10th International Conference on Orange Technology (ICOT), Shanghai, China.","DOI":"10.1109\/ICOT56925.2022.10008164"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s00500-021-06407-8","article-title":"An improved Yolov5 real-time detection method for small objects captured by UAV","volume":"26","author":"Zhan","year":"2022","journal-title":"Soft Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"14365","DOI":"10.1109\/ACCESS.2023.3241005","article-title":"An Improved YOLOv5 Method for Small Object Detection in UAV Capture Scenes","volume":"11","author":"Liu","year":"2023","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kuan, T.-W., Gu, Y., Chen, T., and Shen, Y. (2022, January 10\u201311). Attention-based U-Net extensions for Complex Noises of Smart Campus Road Segmentation. Proceedings of the 2022 10th International Conference on Orange Technology (ICOT), Shanghai, China.","DOI":"10.1109\/ICOT56925.2022.10008109"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yu, X., Kuan, T.W., Qian, Z.Y., and Wang, Q. (2022, January 10\u201311). HSV Semantic Segmentation on Partially Facility and Phanerophyte Sunshine-Shadowing Road. Proceedings of the 2022 10th International Conference on Orange Technology (ICOT), Shanghai, China.","DOI":"10.1109\/ICOT56925.2022.10008157"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sun, Z., Geng, H., Lu, Z., Scherer, R., and Wo\u017aniak, M. (2021). Review of road segmentation for SAR images. Remote Sens., 13.","DOI":"10.3390\/rs13051011"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, J., Qin, Q., Gao, Z., Zhao, J., and Ye, X. (2016). A new approach to urban road extraction using high-resolution aerial image. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5070114"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hui, Z., Hu, Y., Yevenyo, Y.Z., and Yu, X. (2016). An improved morphological algorithm for filtering airborne LiDAR point cloud based on multi-level kriging interpolation. Remote Sens., 8.","DOI":"10.3390\/rs8010035"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and BROX, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the 18th International Conference on Medical Image Computing and Computer Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, L., Wang, C., Zhang, H., Zhang, B., and Wu, F. (2019). Urban building change detection in SAR images using combined differential image and residual u-net network. Remote Sens., 11.","DOI":"10.3390\/rs11091091"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shuai, L., Gao, X., and Wang, J. (2021, January 18\u201320). Wnet++: A nested W-shaped network with multiscale input and adaptive deep supervision for osteosarcoma segmentation. Proceedings of the 2021 IEEE 4th International Conference on Electronic Information and Communication Technology (ICEICT), Xi\u2019an, China.","DOI":"10.1109\/ICEICT53123.2021.9531311"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kamble, R., Samanta, P., and Singhal, N. (2020, January 8). Optic disc, cup and fovea detection from retinal images using U-Net++ with EfficientNet encoder. Proceedings of the Ophthalmic Medical Image Analysis: 7th International Workshop, OMIA 2020, Lima, Peru.","DOI":"10.1007\/978-3-030-63419-3_10"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cui, H., Liu, X., and Huang, N. (2019, January 13\u201317). Pulmonary vessel segmentation based on orthogonal fused u-net++ of chest CT images. Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China.","DOI":"10.1007\/978-3-030-32226-7_33"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., and Ronneberger, O. (2016, January 17\u201321). 3D U-Net: Learning dense volumetric segmentation from sparse annotation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016: 19th International Conference, Athens, Greece.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Isensee, F., and Maier-Hein, K.H. (2019). An attempt at beating the 3D U-Net. arXiv.","DOI":"10.24926\/548719.001"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hwang, H., Rehman, H.Z.U., and Lee, S. (2019). 3D U-Net for skull stripping in brain MRI. Appl. Sci., 9.","DOI":"10.3390\/app9030569"},{"key":"ref_21","unstructured":"Wang, F., Jiang, R., Zheng, L., Meng, C., and Biswal, B. (2019). International MICCAI Brainlesion Workshop, Springer International Publishing."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road extraction by deep residual u-net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yu, G., Dong, J., Wang, Y., and Zhou, X. (2022). RUC-Net: A Residual-Unet-Based Convolutional Neural Network for Pixel-Level Pavement Crack Segmentation. Sensors, 23.","DOI":"10.3390\/s23010053"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103861","DOI":"10.1016\/j.bspc.2022.103861","article-title":"dResU-Net: 3D deep residual U-Net based brain tumor segmentation from multimodal MRI","volume":"79","author":"Rehan","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yang, X., Li, X., Ye, Y., Zhang, X., Zhang, H., Huang, X., and Zhang, B. (2019, January 14\u201319). Road detection via deep residual dense u-net. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8851728"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3219","DOI":"10.1109\/JSTARS.2019.2925841","article-title":"A novel deep structure U-Net for sea-land segmentation in remote sensing images","volume":"12","author":"Shamsolmoali","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, Z., Li, D., Fan, W., Guan, H., Wang, C., and Li, J. (2021). Self-attention in reconstruction bias U-Net for semantic segmentation of building rooftops in optical remote sensing images. Remote Sens., 13.","DOI":"10.3390\/rs13132524"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mustafa, N., Zhao, J., Liu, Z., Zhang, Z., and Yu, W. (October, January 26). Iron ORE region segmentation using high-resolution remote sensing images based on Res-U-Net. Proceedings of the IGARSS 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324218"},{"key":"ref_29","first-page":"1603273","article-title":"U-net: A smart application with multidimensional attention network for remote sensing images","volume":"2022","author":"Wang","year":"2022","journal-title":"Sci. Program."},{"key":"ref_30","unstructured":"Oktay, O., Schlemper, J., Le Folgoc, L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention u-net: Learning where to look for the pancreas. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Petit, O., Thome, N., Rambour, C., Themyr, L., Collins, T., and Soler, L. (2021, January 27). U-net transformer: Self and cross attention for medical image segmentation. Proceedings of the Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Strasbourg, France.","DOI":"10.1007\/978-3-030-87589-3_28"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wu, C., Zhang, F., Xia, J., Xu, Y., Li, G., Xie, J., Du, Z., and Liu, R. (2021). Building damage detection using U-Net with attention mechanism from pre-and post-disaster remote sensing datasets. Remote Sens., 13.","DOI":"10.3390\/rs13050905"},{"key":"ref_33","unstructured":"Iglovikov, V., and Shvets, A. (2018). Ternausnet U-net with vgg11 encoder pre-trained on image net for image segmentation. arXiv."},{"key":"ref_34","unstructured":"Debgupta, R., Chaudhuri, B.B., and Tripathy, B.K. (2019, January 16\u201317). A wide ResNet-based approach for age and gender estimation in face images. Proceedings of the International Conference on Innovative Computing and Communications: Proceedings of ICICC 2019, Bhubaneswar, India."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ali, L., Alnajjar, F., Al Jassmi, H., Gocho, M., Khan, W., and Serhani, M.A. (2021). Performance evaluation of deep CNN-based crack detection and localization techniques for concrete structures. Sensors, 21.","DOI":"10.3390\/s21051688"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Peteinatos, G.G., Reichel, P., Karouta, J., And\u00fajar, D., and Gerhards, R. (2020). Weed identification in maize, sunflower, and potatoes with the aid of convolutional neural networks. Remote Sens., 12.","DOI":"10.3390\/rs12244185"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wickens, C.D., Mccarley, J.S., and Gutzwiller, R.S. (2022). Applied Attention Theory, CRC Press.","DOI":"10.1201\/9781003081579"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.patrec.2008.04.005","article-title":"Semantic object classes in video: A high-definition ground truth database","volume":"30","author":"Brostow","year":"2009","journal-title":"Pattern Recognit. Lett."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/7\/1085\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:14:46Z","timestamp":1760127286000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/7\/1085"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,19]]},"references-count":38,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["e25071085"],"URL":"https:\/\/doi.org\/10.3390\/e25071085","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2023,7,19]]}}}