{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T16:05:06Z","timestamp":1787673906327,"version":"build-2784847793"},"reference-count":47,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T00:00:00Z","timestamp":1699660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2022YFC3800700"],"award-info":[{"award-number":["2022YFC3800700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["42171079"],"award-info":[{"award-number":["42171079"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["2022YFC3800700"],"award-info":[{"award-number":["2022YFC3800700"]}]},{"name":"National Natural Science Foundation of China","award":["42171079"],"award-info":[{"award-number":["42171079"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Street view images are emerging as new street-level sources of urban environmental information. Accurate detection and quantification of urban air conditioners is crucial for evaluating the resilience of urban residential areas to heat wave disasters and formulating effective disaster prevention policies. Utilizing street view image data to predict the spatial coverage of urban air conditioners offers a simple and effective solution. However, detecting and accurately counting air conditioners in complex street-view environments remains challenging. This study introduced 3D parameter-free attention and coordinate attention modules into the target detection process to enhance the extraction of detailed features of air conditioner external units. It also integrated a small target detection layer to address the challenge of detecting small target objects that are easily missed. As a result, an improved algorithm named SC4-YOLOv7 was developed for detecting and recognizing air conditioner external units in street view images. To validate this new algorithm, we extracted air conditioner external units from street view images of residential buildings in Guilin City, Guangxi Zhuang Autonomous Region, China. The results of the study demonstrated that SC4-YOLOv7 significantly improved the average accuracy of recognizing air conditioner external units in street view images from 87.93% to 91.21% compared to the original YOLOv7 method while maintaining a high speed of image recognition detection. The algorithm has the potential to be extended to various applications requiring small target detection, enabling reliable detection and recognition in real street environments.<\/jats:p>","DOI":"10.3390\/s23229118","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T02:46:47Z","timestamp":1699843607000},"page":"9118","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An Improved New YOLOv7 Algorithm for Detecting Building Air Conditioner External Units from Street View Images"],"prefix":"10.3390","volume":"23","author":[{"given":"Zhongmin","family":"Tian","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research of Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donghong","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"137678","DOI":"10.1016\/j.scitotenv.2020.137678","article-title":"Defining heat waves and extreme heat events using sub-regional meteorological data to maximize benefits of early warning systems to population health","volume":"721","author":"McElroy","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1097\/EDE.0000000000000375","article-title":"Vulnerability to heat-related mortality: A systematic review, meta-analysis, and meta-regression analysis","volume":"26","author":"Benmarhnia","year":"2015","journal-title":"Epidemiology"},{"key":"ref_3","first-page":"12500","article-title":"Magnitude of extreme heat waves in present climate and their projection in a warming world","volume":"119","author":"Russo","year":"2015","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"015004","DOI":"10.1088\/2515-7620\/ab6933","article-title":"Asian megacity heat stress under future climate scenarios: Impact of air-conditioning feedback","volume":"2","author":"Takane","year":"2020","journal-title":"Environ. Res. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1097\/EDE.0000000000001241","article-title":"Air conditioning and heat-related mortality: A multi-country longitudinal study","volume":"31","author":"Sera","year":"2020","journal-title":"Epidemiology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1016\/S0140-6736(21)01787-6","article-title":"The 2021 report of the lancet countdown on health and climate change: Code red for a healthy future","volume":"398","author":"Romanello","year":"2021","journal-title":"Lancet"},{"key":"ref_7","unstructured":"International Energy Agency (2023). Sustainable, Affordable Cooling Can Save Tens of Thousands of Lives Each Year, IEA. Available online: https:\/\/www.iea.org\/reports\/sustainable-affordable-cooling-can-save-tens-of-thousands-of-lives-each-year."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Goel, R., Garcia, L.M.T., Goodman, A., Johnson, R., Aldred, R., Murugesan, M., Brage, S., Bhalla, K., and Woodcock, J. (2018). Estimating city-level travel patterns using street imagery: A case study of using Google Street View in Britain. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0196521"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.cities.2015.03.006","article-title":"Understanding urban China with open data","volume":"47","author":"Liu","year":"2015","journal-title":"Cities"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.ufug.2015.06.006","article-title":"Assessing street-level urban greenery using Google Street View and a modified green view index","volume":"14","author":"Li","year":"2015","journal-title":"Urban For. Urban Green."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.isprsjprs.2019.04.017","article-title":"Social sensing from street-level imagery: A case study in learning spatio-temporal urban mobility patterns","volume":"153","author":"Zhang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Supporting online material for \u201cReducing the dimensionality of data with neural networks\u201d","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_13","first-page":"1","article-title":"Deep Neural Networks for object detection","volume":"26","author":"Szegedy","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation tech report (v5). Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2520","DOI":"10.1002\/eqe.3805","article-title":"Instance segmentation of soft-story buildings from street-view images with semiautomatic annotation","volume":"52","author":"Wang","year":"2023","journal-title":"Earthq. Eng. Struct. Dyn."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1007\/s11803-020-0598-2","article-title":"Rapid visual screening of soft-story buildings from street view images using deep learning classification","volume":"19","author":"Yu","year":"2020","journal-title":"Earthq. Eng. Eng. Vib."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103968","DOI":"10.1016\/j.autcon.2021.103968","article-title":"Automatic detection of unreinforced masonry buildings from street view images using deep learning-based image segmentation","volume":"132","author":"Wang","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"04022024","DOI":"10.1061\/(ASCE)CP.1943-5487.0001034","article-title":"AdaLN: A Vision Transformer for Multidomain Learning and Predisaster Building Information Extraction from Images","volume":"36","author":"Guo","year":"2022","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2022.07.020","article-title":"Spatial context-aware method for urban land use classification using street view images","volume":"192","author":"Fang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","first-page":"102794","article-title":"Multi-modal fusion of satellite and street-view images for urban village classification based on a dual-branch deep neural network","volume":"109","author":"Chen","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"13108","DOI":"10.1073\/pnas.1700035114","article-title":"Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States","volume":"114","author":"Gebru","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Krylov, V.A., Kenny, E., and Dahyot, R. (2017). Automatic discovery and geoagging of objects from street view imagery. Remote Sens., 10.","DOI":"10.3390\/rs10050661"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106192","DOI":"10.1016\/j.ecolind.2020.106192","article-title":"Quantifying the green view indicator for assessing urban greening quality: An analysis based on Internet-crawling street view data","volume":"113","author":"Chen","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1136\/jech-2017-209456","article-title":"Neighbourhood looking glass: 360\u00ba automated characterisation of the built environment for neighbourhood effects research","volume":"72","author":"Nguyen","year":"2018","journal-title":"J. Epidemiol. Community Health"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104710","DOI":"10.1016\/j.ssci.2020.104710","article-title":"Investigation of clusters and injuries in pedestrian crashes using GIS in Changsha, China","volume":"127","author":"Hu","year":"2020","journal-title":"Saf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, F., and Wang, M. (2021). Deep Learning-Based Method for Detection of External Air Conditioner Units from Street View Images. Remote Sens., 13.","DOI":"10.3390\/rs13183691"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2022). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. arXiv.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_29","unstructured":"Yang, L., Zhang, R.Y., Li, L., and Xie, X. (2021, January 18\u201324). SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks. Proceedings of the 38th International Conference on Machine Learning, PMLR, Virtual Event."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jiang, K., Xie, T., Yan, R., Wen, X., Li, D., Jiang, H., Jiang, N., Feng, L., Duan, X., and Wang, J. (2022). An Attention Mechanism-Improved YOLOv7 Object Detection Algorithm for Hemp Duck Count Estimation. Agriculture, 12.","DOI":"10.3390\/agriculture12101659"},{"key":"ref_32","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao HY, M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ni, Y., Mao, J., Fu, Y., Wang, H., Zong, H., and Luo, K. (2023). Damage Detection and Localization of Bridge Deck Pavement Based on Deep Learning. Sensors, 23.","DOI":"10.3390\/s23115138"},{"key":"ref_34","first-page":"23","article-title":"Corroded and loosened bolt detection of steel bolted joints based on improved you only look once network and line segment detector","volume":"32","author":"Ni","year":"2023","journal-title":"Smart Struct. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zeng, H., Guo, S., and Zhang, L. (2022, January 23\u201327). Efficient Long-Range Attention Network for Image Super-Resolution. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19790-1_39"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., and Wu, E. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 14\u201319). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018). CBAM: Convolutional Block Attention Module. arXiv.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.landurbplan.2018.08.020","article-title":"Measuring human perceptions of a large-scale urban region using machine learning","volume":"180","author":"Zhang","year":"2018","journal-title":"Landsc. Urban Plan."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Liao, H.Y.M., Wu, Y.H., Chen, P.Y., Hsieh, J.W., and Yeh, I.H. (2020, January 13\u201319). CSPNet: A New Backbone that can Enhance Learning Capability of CNN. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_41","unstructured":"Van Etten, A. (2018). You only look twice: Rapid multi-scale object detection in satellite imagery. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2486","DOI":"10.1109\/TGRS.2016.2645610","article-title":"Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks","volume":"55","author":"Long","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/LGRS.2018.2872355","article-title":"Multiscale Visual Attention Networks for Object Detection in VHR Remote Sensing Images","volume":"16","author":"Wang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ferrari, L., Rosi, A., Mamei, M., and Zambonelli, F. (2011, January 1). Extracting urban patterns from location-based social networks. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks, Chicago, IL, USA.","DOI":"10.1145\/2063212.2063226"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Yu, L., Zhengwei, S., Chaogui, K., Yong, G., and Peter, C. (2014). Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0086026"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s10109-012-0166-z","article-title":"Understanding intra-urban trip patterns from taxi trajectory data","volume":"14","author":"Liu","year":"2012","journal-title":"J. Geogr. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hoffmann, E.J., Wang, Y., Werner, M., Kang, J., and Zhu, X.X. (2019). Model Fusion for Building Type Classification from Aerial and Street View Images. Remote Sens., 11.","DOI":"10.3390\/rs11111259"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9118\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:21:34Z","timestamp":1760131294000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9118"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,11]]},"references-count":47,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["s23229118"],"URL":"https:\/\/doi.org\/10.3390\/s23229118","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,11]]}}}