{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T15:48:19Z","timestamp":1778255299784,"version":"3.51.4"},"reference-count":29,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,12,5]],"date-time":"2021-12-05T00:00:00Z","timestamp":1638662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873196, 61772187"],"award-info":[{"award-number":["61873196, 61772187"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to the complexity and danger of Mars\u2019s environment, traditional Mars unmanned ground vehicles cannot efficiently perform Mars exploration missions. To solve this problem, the DeepLabV3+\/Efficientnet hybrid network is proposed and applied to the scene area judgment for the Mars unmanned vehicle system. Firstly, DeepLabV3+ is used to extract the feature information of the Mars image due to its high accuracy. Then, the feature information is used as the input for Efficientnet, and the categories of scene areas are obtained, including safe area, report area, and dangerous area. Finally, according to three categories, the Mars unmanned vehicle system performs three operations: pass, report, and send. Experimental results show the effectiveness of the DeepLabV3+\/Efficientnet hybrid network in the scene area judgment. Compared with the Efficientnet network, the accuracy of the DeepLabV3+\/Efficientnet hybrid network is improved by approximately 18% and reaches 99.84%, which ensures the safety of the exploration mission for the Mars unmanned vehicle system.<\/jats:p>","DOI":"10.3390\/s21238136","type":"journal-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T03:10:38Z","timestamp":1638760238000},"page":"8136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["DeepLabV3+\/Efficientnet Hybrid Network-Based Scene Area Judgment for the Mars Unmanned Vehicle System"],"prefix":"10.3390","volume":"21","author":[{"given":"Shuang","family":"Hu","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwei","family":"Kang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2469","DOI":"10.1109\/TAES.2017.2700958","article-title":"Solar flare TDOA navigation method using direct and reflected light for mars exploration","volume":"53","author":"Liu","year":"2017","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107194","DOI":"10.1016\/j.compeleceng.2021.107194","article-title":"A fast instance segmentation with one-stage multi-task deep neural network for autonomous driving","volume":"93","author":"Tseng","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1177\/0278364907073777","article-title":"Tradeoffs between directed and autonomous driving on the Mars exploration rovers","volume":"26","author":"Biesiadecki","year":"2007","journal-title":"Int. J. Robot. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Simon, M., Latorella, K., Martin, J., Cerro, J., Lepsch, R., Jefferies, S., Goodliff, K., Smitherman, D., McCleskey, C., and Stromgre, C. (2017, January 4\u201311). NASA\u2019s advanced exploration systems Mars transit habitat refinement point of departure design. Proceedings of the 2017 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2017.7943662"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107135","DOI":"10.1016\/j.compeleceng.2021.107135","article-title":"Approaches for exploration of improving multi-slice mapping via forwarding intersection based on images of UAV oblique photogrammetry","volume":"92","author":"Yang","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/TSMC.2016.2582745","article-title":"Vehicle routing problems for drone delivery","volume":"47","author":"Dorling","year":"2016","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.compeleceng.2018.04.003","article-title":"A road segmentation method based on the deep auto-encoder with supervised learning","volume":"68","author":"Song","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1109\/TGRS.2016.2642125","article-title":"Kernel slow feature analysis for scene change detection","volume":"55","author":"Wu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","unstructured":"Simonyan, K., and Andrew, Z. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, Y., Xu, C., Shi, B., Xu, C., Tian, Q., and Xu, C. (2020, January 13\u201319). AdderNet: Do we really need multiplications in deep learning?. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00154"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_13","unstructured":"Tan, M., and Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. International Conference on Machine Learning, PMLR."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, M., Yu, K., Zhang, C., Li, Z., and Yang, K. (2018, January 18\u201323). Denseaspp for semantic segmentation in street scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00388"},{"key":"ref_16","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, J., Li, K., and Yan, B. (2019, January 8\u201312). Scale-aware deep network with hole convolution for blind motion deblurring. Proceedings of the 2019 IEEE International Conference on Multimedia and Expo (ICME), Shanghai, China.","DOI":"10.1109\/ICME.2019.00119"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"He, H., Yang, D., Wang, S., Wang, S., and Li, Y. (2019). Road extraction by using atrous spatial pyramid pooling integrated encoder-decoder network and structural similarity loss. Remote Sens., 11.","DOI":"10.3390\/rs11091015"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Guo, Y., Chen, J., Wang, J., Chen, Q., Cao, J., Deng, Z., Xu, Y., and Tan, M. (2020, January 13\u201319). Closed-loop matters: Dual regression networks for single image super-resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00545"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.procs.2019.08.147","article-title":"Mobilenet convolutional neural networks and support vector machines for palmprint recognition","volume":"157","author":"Michele","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107196","DOI":"10.1016\/j.compeleceng.2021.107196","article-title":"Intelligent interaction model for battleship control based on the fusion of target intention and operator emotion","volume":"92","author":"Wang","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5858","DOI":"10.1109\/ACCESS.2017.2696121","article-title":"Smart augmentation learning an optimal data augmentation strategy","volume":"5","author":"Lemley","year":"2017","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/23\/8136\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:40:00Z","timestamp":1760168400000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/23\/8136"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,5]]},"references-count":29,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["s21238136"],"URL":"https:\/\/doi.org\/10.3390\/s21238136","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,5]]}}}