{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T20:48:31Z","timestamp":1775076511035,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T00:00:00Z","timestamp":1667952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["101006817"],"award-info":[{"award-number":["101006817"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Object detection is recognized as one of the most critical research areas for the perception of self-driving cars. Current vision systems combine visible imaging, LIDAR, and\/or RADAR technology, allowing perception of the vehicle\u2019s surroundings. However, harsh weather conditions mitigate the performances of these systems. Under these circumstances, thermal imaging becomes the complementary solution to current systems not only because it makes it possible to detect and recognize the environment in the most extreme conditions, but also because thermal images are compatible with detection and recognition algorithms, such as those based on artificial neural networks. In this paper, an analysis of the resilience of thermal sensors in very unfavorable fog conditions is presented. The goal was to study the operational limits, i.e., the very degraded fog situation beyond which a thermal camera becomes unreliable. For the analysis, the mean pixel intensity and the contrast were used as indicators. Results showed that the angle of view (AOV) of a thermal camera is a determining parameter for object detection in foggy conditions. Additionally, results show that cameras with AOVs 18\u00b0 and 30\u00b0 are suitable for object detection, even under thick fog conditions (from 13 m meteorological optical range). These results were extended using object detection software, with which it is shown that, for the pedestrian, a detection rate \u226590% was achieved using the images from the 18\u00b0 and 30\u00b0 cameras.<\/jats:p>","DOI":"10.3390\/jimaging8110306","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T02:03:58Z","timestamp":1668045838000},"page":"306","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Analysis of Thermal Imaging Performance under Extreme Foggy Conditions: Applications to Autonomous Driving"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1529-3016","authenticated-orcid":false,"given":"Josu\u00e9 Manuel","family":"Rivera Vel\u00e1zquez","sequence":"first","affiliation":[{"name":"Cerema Occitanie, Research Team \u201cIntelligent Transport Systems\u201d, 1 Avenue du Colonel Roche, 31400 Toulouse, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5947-4302","authenticated-orcid":false,"given":"Louahdi","family":"Khoudour","sequence":"additional","affiliation":[{"name":"Cerema Occitanie, Research Team \u201cIntelligent Transport Systems\u201d, 1 Avenue du Colonel Roche, 31400 Toulouse, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2375-3649","authenticated-orcid":false,"given":"Guillaume","family":"Saint Pierre","sequence":"additional","affiliation":[{"name":"Cerema Occitanie, Research Team \u201cIntelligent Transport Systems\u201d, 1 Avenue du Colonel Roche, 31400 Toulouse, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6705-1131","authenticated-orcid":false,"given":"Pierre","family":"Duthon","sequence":"additional","affiliation":[{"name":"Cerema Centre-Est, Research Team \u201cIntelligent Transport Systems\u201d, 8-10, Rue Bernard Palissy, 63017 Clermont-Ferrand, France"}]},{"given":"S\u00e9bastien","family":"Liandrat","sequence":"additional","affiliation":[{"name":"Cerema Centre-Est, Research Team \u201cIntelligent Transport Systems\u201d, 8-10, Rue Bernard Palissy, 63017 Clermont-Ferrand, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1248-153X","authenticated-orcid":false,"given":"Fr\u00e9d\u00e9ric","family":"Bernardin","sequence":"additional","affiliation":[{"name":"Cerema Centre-Est, Research Team \u201cIntelligent Transport Systems\u201d, 8-10, Rue Bernard Palissy, 63017 Clermont-Ferrand, France"}]},{"given":"Sharon","family":"Fiss","sequence":"additional","affiliation":[{"name":"ADASKY, 7 Hamada Street, Yokneam Illit 2069206, Israel"}]},{"given":"Igor","family":"Ivanov","sequence":"additional","affiliation":[{"name":"ADASKY, 7 Hamada Street, Yokneam Illit 2069206, Israel"}]},{"given":"Raz","family":"Peleg","sequence":"additional","affiliation":[{"name":"ADASKY, 7 Hamada Street, Yokneam Illit 2069206, Israel"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,9]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (WHO) (2022, March 21). 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