{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T08:13:16Z","timestamp":1777104796387,"version":"3.51.4"},"reference-count":31,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T00:00:00Z","timestamp":1754870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Committee of the Minister of Science and Higher Education of the Republic of Kazakhstan","award":["AP19678989"],"award-info":[{"award-number":["AP19678989"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Urban safety critically depends on effective street lighting systems; however, rapidly expanding cities, such as Astana, face considerable challenges in maintaining these systems due to the inefficiency, high labor intensity, and error-prone nature of conventional manual inspection methods. This necessitates an urgent shift toward automated, accurate, and scalable monitoring systems capable of quickly identifying malfunctioning streetlights. In response, this study introduces an advanced computer vision-based approach for automated detection and analysis of street lighting conditions. Leveraging high-resolution dashcam footage collected under diverse nighttime weather conditions, we constructed a robust dataset of 4260 carefully annotated frames highlighting streetlight poles and lamps. To significantly enhance detection accuracy, we propose the novel YOLO-CSE model, which integrates a Channel Squeeze-and-Excitation (CSE) module into the YOLO (You Only Look Once) detection architecture. The CSE module leverages the inherent symmetry of streetlight structures, such as the bilateral symmetry of poles and the radial symmetry of lamps, to dynamically recalibrate feature channels, emphasizing spatially repetitive and geometrically uniform patterns. By modifying the bottleneck layer through the addition of an extra convolutional layer and the SE block, the model learns richer, more discriminative feature representations, particularly for small or distant lamps under partial occlusion or low illumination. A comprehensive comparative analysis demonstrates that YOLO-CSE outperforms conventional YOLO variants and state-of-the-art models, achieving a mean average precision (mAP) of 0.798, recall of 0.794, precision of 0.824, and an F1 score of 0.808. The model\u2019s symmetry-aware design enhances robustness to urban clutter (e.g., asymmetric noise from headlights or signage) while maintaining real-time efficiency. These results validate YOLO-CSE as a scalable solution for smart cities, where symmetry principles bridge geometric priors with computational efficiency in infrastructure monitoring.<\/jats:p>","DOI":"10.3390\/sym17081294","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T14:32:36Z","timestamp":1754922756000},"page":"1294","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Applying Computer Vision for the Detection and Analysis of the Condition and Operation of Street Lighting"],"prefix":"10.3390","volume":"17","author":[{"given":"Sunggat","family":"Aiymbay","sequence":"first","affiliation":[{"name":"Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana 010000, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1042-0415","authenticated-orcid":false,"given":"Ainur","family":"Zhumadillayeva","sequence":"additional","affiliation":[{"name":"Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana 010000, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric T.","family":"Matson","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Technology, Purdue University, West Lafayette, IN 47907-2021, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0775-7324","authenticated-orcid":false,"given":"Bakhyt","family":"Matkarimov","sequence":"additional","affiliation":[{"name":"Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana 010000, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bigul","family":"Mukhametzhanova","sequence":"additional","affiliation":[{"name":"Department of Information and Computing System, Abylkas Saginov Karaganda Technical University, Karaganda 100000, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4981","DOI":"10.1109\/JSEN.2016.2552249","article-title":"Urban street lighting infrastructure monitoring using a mobile sensor platform","volume":"16","author":"Kumar","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_2","unstructured":"Natu, O. (2025, March 02). GSM Based Smart Street Light Monitoring and Control System. 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