{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T20:12:40Z","timestamp":1777407160022,"version":"3.51.4"},"reference-count":21,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T00:00:00Z","timestamp":1664064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"the National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFD1101101"],"award-info":[{"award-number":["2019YFD1101101"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"the National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFD1101105"],"award-info":[{"award-number":["2019YFD1101105"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"the National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["F2022204004"],"award-info":[{"award-number":["F2022204004"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"the National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["20327402D,19227210D"],"award-info":[{"award-number":["20327402D,19227210D"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"the National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFD1101101"],"award-info":[{"award-number":["2019YFD1101101"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"the National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFD1101105"],"award-info":[{"award-number":["2019YFD1101105"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"the National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["F2022204004"],"award-info":[{"award-number":["F2022204004"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"the National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["20327402D,19227210D"],"award-info":[{"award-number":["20327402D,19227210D"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Natural Science Foundation of Hebei Province","award":["2019YFD1101101"],"award-info":[{"award-number":["2019YFD1101101"]}]},{"name":"the Natural Science Foundation of Hebei Province","award":["2019YFD1101105"],"award-info":[{"award-number":["2019YFD1101105"]}]},{"name":"the Natural Science Foundation of Hebei Province","award":["F2022204004"],"award-info":[{"award-number":["F2022204004"]}]},{"name":"the Natural Science Foundation of Hebei Province","award":["20327402D,19227210D"],"award-info":[{"award-number":["20327402D,19227210D"]}]},{"name":"the Hebei Province Key Research and Development Program","award":["2019YFD1101101"],"award-info":[{"award-number":["2019YFD1101101"]}]},{"name":"the Hebei Province Key Research and Development Program","award":["2019YFD1101105"],"award-info":[{"award-number":["2019YFD1101105"]}]},{"name":"the Hebei Province Key Research and Development Program","award":["F2022204004"],"award-info":[{"award-number":["F2022204004"]}]},{"name":"the Hebei Province Key Research and Development Program","award":["20327402D,19227210D"],"award-info":[{"award-number":["20327402D,19227210D"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The governance of rural living environments is one of the important tasks in the implementation of a rural revitalization strategy. At present, the illegal behaviors of random construction and random storage in public spaces have seriously affected the effectiveness of the governance of rural living environments. The current supervision on such problems mainly relies on manual inspection. Due to the large number and wide distribution of rural areas to be inspected, this method is limited by obvious disadvantages, such as low detection efficiency, long-time spending, and huge consumption of human resources, so it is difficult to meet the requirements of efficient and accurate inspection. In response to the difficulties encountered, a low-altitude remote sensing inspection method on rural living environments was proposed based on a modified YOLOv5s-ViT (YOLOv5s-Vision Transformer) in this paper. First, the BottleNeck structure was modified to enhance the multi-scale feature capture capability of the model. Then, the SimAM attention mechanism module was embedded to intensify the model\u2019s attention to key features without increasing the number of parameters. Finally, the Vision Transformer component was incorporated to improve the model\u2019s ability to perceive global features in the image. The testing results of the established model showed that, compared with the original YOLOv5 network, the Precision, Recall, and mAP of the modified YOLOv5s-ViT model improved by 2.2%, 11.5%, and 6.5%, respectively; the total number of parameters was reduced by 68.4%; and the computation volume was reduced by 83.3%. Relative to other mainstream detection models, YOLOv5s-ViT achieved a good balance between detection performance and model complexity. This study provides new ideas for improving the digital capability of the governance of rural living environments.<\/jats:p>","DOI":"10.3390\/rs14194784","type":"journal-article","created":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T03:34:17Z","timestamp":1664163257000},"page":"4784","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Low-Altitude Remote Sensing Inspection Method on Rural Living Environments Based on a Modified YOLOv5s-ViT"],"prefix":"10.3390","volume":"14","author":[{"given":"Chunshan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"Hebei Key Laboratory of Agricultural Big Data, Baoding 071001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China"},{"name":"Hebei Key Laboratory of Agricultural Big Data, Baoding 071001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huarui","family":"Wu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunjiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guifa","family":"Teng","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China"},{"name":"Hebei Key Laboratory of Agricultural Big Data, Baoding 071001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingru","family":"Yang","sequence":"additional","affiliation":[{"name":"Shijiazhuang Academy of Agriculture and Forestry Sciences, Shijiazhuang 050041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Du","sequence":"additional","affiliation":[{"name":"Shijiazhuang Academy of Agriculture and Forestry Sciences, Shijiazhuang 050041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,25]]},"reference":[{"key":"ref_1","unstructured":"National Bureau of Statistics of China (2021). 2021 China Statistical Yearbook."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned aerial systems for photogrammetry and remote sensing: A review","volume":"92","author":"Colomina","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yao, H., Qin, R., and Chen, X. (2019). Unmanned aerial vehicle for remote sensing applications\u2014A review. Remote Sens., 11.","DOI":"10.3390\/rs11121443"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8448","DOI":"10.1007\/s10489-021-02893-3","article-title":"RSOD: Real-time small object detection algorithm in UAV-based traffic monitoring","volume":"52","author":"Sun","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Byun, S., Shin, I.-K., Moon, J., Kang, J., and Choi, S.-I. (2021). Road traffic monitoring from UAV images using deep learning networks. Remote Sens., 13.","DOI":"10.3390\/rs13204027"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1177\/0361198119847991","article-title":"Use of multi-rotor unmanned aerial vehicles for fine-grained roadside air pollution monitoring","volume":"2673","author":"Li","year":"2019","journal-title":"Transp. Res. Rec."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bolla, G.M., Casagrande, M., Comazzetto, A., Dal Moro, R., Destro, M., Fantin, E., Colombatti, G., Aboudan, A., and Lorenzini, E.C. (2018, January 20\u201322). ARIA: Air pollutants monitoring using UAVs. Proceedings of the 2018 5th IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace), Rome, Italy.","DOI":"10.1109\/MetroAeroSpace.2018.8453584"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9305","DOI":"10.1007\/s13369-021-05522-w","article-title":"Power transmission line fault detection and diagnosis based on artificial intelligence approach and its development in UAV: A review","volume":"46","author":"Wong","year":"2021","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, W., Li, Y., and Zhao, Z. (2022). Transmission Line Vibration Damper Detection Using Deep Neural Networks Based on UAV Remote Sensing Image. Sensors, 22.","DOI":"10.3390\/s22051892"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, F., Xia, J., Xie, J., Zhang, Z., Du, Z., and Liu, R. (2021). Identifying Damaged Buildings in Aerial Images Using the Object Detection Method. Remote Sens., 13.","DOI":"10.3390\/rs13214213"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, R., Li, H., Duan, K., You, S., Liu, K., Wang, F., and Hu, Y. (2020). Automatic detection of earthquake-damaged buildings by integrating UAV oblique photography and infrared thermal imaging. Remote Sens., 12.","DOI":"10.3390\/rs12162621"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object detection with deep learning: A review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2020.01.085","article-title":"Recent advances in deep learning for object detection","volume":"396","author":"Wu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3239","DOI":"10.1109\/TPAMI.2021.3051099","article-title":"Salient object detection in the deep learning era: An in-depth survey","volume":"44","author":"Wang","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jiao, Z., Zhang, Y., Xin, J., Mu, L., Yi, Y., Liu, H., and Liu, D. (2019, January 23\u201327). A deep learning based forest fire detection approach using UAV and YOLOv3. Proceedings of the 2019 1st International Conference on Industrial Artificial Intelligence (IAI), Shenyang, China.","DOI":"10.1109\/ICIAI.2019.8850815"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ammour, N., Alhichri, H., Bazi, Y., Ben Jdira, B., Alajlan, N., and Zuair, M. (2017). Deep learning approach for car detection in UAV imagery. Remote Sens., 9.","DOI":"10.3390\/rs9040312"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"164214","DOI":"10.1109\/ACCESS.2020.3022419","article-title":"An automatic detection method of bird\u2019s nest on transmission line tower based on faster_RCNN","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ma, H., Liu, Y., Ren, Y., and Yu, J. (2019). Detection of collapsed buildings in post-earthquake remote sensing images based on the improved YOLOv3. Remote Sens., 12.","DOI":"10.3390\/rs12010044"},{"key":"ref_19","first-page":"236","article-title":"Detection method of illegal building based on YOLOv5","volume":"57","author":"Yu","year":"2021","journal-title":"Comput. Eng. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jiang, H., Hu, X., Li, K., Zhang, J., Gong, J., and Zhang, M. (2020). PGA-SiamNet: Pyramid feature-based attention-guided Siamese network for remote sensing orthoimagery building change detection. Remote Sens., 12.","DOI":"10.3390\/rs12030484"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Peng, B., Ren, D., Zheng, C., and Lu, A. (2022). TRDet: Two-Stage Rotated Detection of Rural Buildings in Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14030522"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4784\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:39:01Z","timestamp":1760143141000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4784"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,25]]},"references-count":21,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14194784"],"URL":"https:\/\/doi.org\/10.3390\/rs14194784","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,25]]}}}