{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:25:38Z","timestamp":1773156338431,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,14]],"date-time":"2024-01-14T00:00:00Z","timestamp":1705190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education","award":["NRF-2021R1A2B5B02086773"],"award-info":[{"award-number":["NRF-2021R1A2B5B02086773"]}]},{"name":"Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education","award":["NRF-2021R1A6A1A03039493"],"award-info":[{"award-number":["NRF-2021R1A6A1A03039493"]}]},{"name":"Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education","award":["NRF-2022R1I1A1A01070998"],"award-info":[{"award-number":["NRF-2022R1I1A1A01070998"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recent advancements in computer vision technology, developments in sensors and sensor-collecting approaches, and the use of deep and transfer learning approaches have excelled in the development of autonomous vehicles. On-road vehicle detection has become a task of significant importance, especially due to exponentially increasing research on autonomous vehicles during the past few years. With high-end computing resources, a large number of deep learning models have been trained and tested for on-road vehicle detection recently. Vehicle detection may become a challenging process especially due to varying light and weather conditions like night, snow, sand, rain, foggy conditions, etc. In addition, vehicle detection should be fast enough to work in real time. This study investigates the use of the recent YOLO version, YOLOx, to detect vehicles in bad weather conditions including rain, fog, snow, and sandstorms. The model is tested on the publicly available benchmark dataset DAWN containing images containing four bad weather conditions, different illuminations, background, and number of vehicles in a frame. The efficacy of the model is evaluated in terms of precision, recall, and mAP. The results exhibit the better performance of YOLOx-s over YOLOx-m and YOLOx-l variants. YOLOx-s has 0.8983 and 0.8656 mAP for snow and sandstorms, respectively, while its mAP for rain and fog is 0.9509 and 0.9524, respectively. The performance of models is better for snow and foggy weather than rainy weather sandstorms. Further experiments indicate that enhancing image quality using multiscale retinex improves YOLOx performance.<\/jats:p>","DOI":"10.3390\/s24020522","type":"journal-article","created":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T07:25:07Z","timestamp":1705303507000},"page":"522","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Analyzing Performance of YOLOx for Detecting Vehicles in Bad Weather Conditions"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8271-6496","authenticated-orcid":false,"given":"Imran","family":"Ashraf","sequence":"first","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea"}]},{"given":"Soojung","family":"Hur","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7021-0105","authenticated-orcid":false,"given":"Gunzung","family":"Kim","sequence":"additional","affiliation":[{"name":"Institute of Information and Communication, Yeungnam University, Gyeongsan 38541, Republic of Korea"}]},{"given":"Yongwan","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,14]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2023, September 25). Road Traffic Injuries, Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/road-traffic-injuries."},{"key":"ref_2","unstructured":"National Highway Traffic Safety Administration (2008). National motor vehicle crash causation survey: Report to congress. Natl. Highw. Traffic Saf. Adm. Tech. Rep. Dot, 811, 059."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ashraf, I., Hur, S., Shafiq, M., and Park, Y. (2019). Catastrophic factors involved in road accidents: Underlying causes and descriptive analysis. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0223473"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1016\/j.sigpro.2014.10.035","article-title":"Towards improving quality of video-based vehicle counting method for traffic flow estimation","volume":"120","author":"Xia","year":"2016","journal-title":"Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3811","DOI":"10.1109\/TNNLS.2021.3128968","article-title":"A review of vehicle detection techniques for intelligent vehicles","volume":"34","author":"Wang","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2020\/6632956","article-title":"Vehicle detection based on multifeature extraction and recognition adopting RBF neural network on ADAS system","volume":"2020","author":"Chen","year":"2020","journal-title":"Complexity"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4297","DOI":"10.1109\/TITS.2016.2614545","article-title":"Looking at vehicles in the night: Detection and dynamics of rear lights","volume":"20","author":"Satzoda","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4794","DOI":"10.1016\/j.ijleo.2016.01.017","article-title":"Multiscale edge fusion for vehicle detection based on difference of Gaussian","volume":"127","author":"Mu","year":"2016","journal-title":"Optik"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Shao, H.X., and Duan, X.M. (2012). Video Vehicle Detection Method Based on Multiple Color Space Information Fusion, Trans Tech Publications. Advanced Materials Research.","DOI":"10.4028\/www.scientific.net\/AMR.546-547.721"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1007\/s00138-011-0355-7","article-title":"Symmetry-based monocular vehicle detection system","volume":"23","author":"Teoh","year":"2012","journal-title":"Mach. Vis. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cao, X., Wu, C., Yan, P., and Li, X. (2011, January 11\u201314). Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos. Proceedings of the 2011 18th IEEE International Conference on Image Processing, Brussels, Belgium.","DOI":"10.1109\/ICIP.2011.6116132"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1109\/TITS.2012.2187894","article-title":"On-road multivehicle tracking using deformable object model and particle filter with improved likelihood estimation","volume":"13","author":"Niknejad","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_13","first-page":"508","article-title":"Efficient feature selection and classification for vehicle detection","volume":"25","author":"Wen","year":"2014","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/TITS.2013.2294646","article-title":"Symmetrical SURF and its applications to vehicle detection and vehicle make and model recognition","volume":"15","author":"Hsieh","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, X., and Meng, Q. (2013, January 13\u201316). Vehicle detection from UAVs by using SIFT with implicit shape model. Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester, UK.","DOI":"10.1109\/SMC.2013.535"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1186\/s12544-019-0390-4","article-title":"Vision-based vehicle detection and counting system using deep learning in highway scenes","volume":"11","author":"Song","year":"2019","journal-title":"Eur. Transp. Res. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MITS.2019.2903518","article-title":"A comparative study of state-of-the-art deep learning algorithms for vehicle detection","volume":"11","author":"Wang","year":"2019","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., and Garcia-Rodriguez, J. (2017). A review on deep learning techniques applied to semantic segmentation. arXiv.","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4230","DOI":"10.1109\/TITS.2020.3014013","article-title":"Vehicle detection and tracking in adverse weather using a deep learning framework","volume":"22","author":"Hassaballah","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, X.Z., Chang, C.M., Yu, C.W., and Chen, Y.L. (2020). A real-time vehicle detection system under various bad weather conditions based on a deep learning model without retraining. Sensors, 20.","DOI":"10.3390\/s20205731"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"25985","DOI":"10.1007\/s11042-021-10954-5","article-title":"On-road vehicle detection in varying weather conditions using faster R-CNN with several region proposal networks","volume":"80","author":"Ghosh","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_22","unstructured":"Canziani, A., Paszke, A., and Culurciello, E. (2016). An analysis of deep neural network models for practical applications. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"110737","DOI":"10.1016\/j.measurement.2022.110737","article-title":"A new scheme of vehicle detection for severe weather based on multi-sensor fusion","volume":"191","author":"Wang","year":"2022","journal-title":"Measurement"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1109\/TITS.2011.2181366","article-title":"Adaptive vehicle detector approach for complex environments","volume":"13","author":"Wu","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Singh, A., Kumar, D.P., Shivaprasad, K., Mohit, M., and Wadhawan, A. (2021, January 4\u20135). Vehicle detection and accident prediction in sand\/dust storms. Proceedings of the 2021 International Conference on Computing Sciences (ICCS), Phagwara, India.","DOI":"10.1109\/ICCS54944.2021.00029"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Humayun, M., Ashfaq, F., Jhanjhi, N.Z., and Alsadun, M.K. (2022). Traffic management: Multi-scale vehicle detection in varying weather conditions using yolov4 and spatial pyramid pooling network. Electronics, 11.","DOI":"10.3390\/electronics11172748"},{"key":"ref_27","first-page":"012015","article-title":"Vehicle detection in foggy weather based on an enhanced YOLO method","volume":"Volume 2284","author":"Li","year":"2022","journal-title":"Proceedings of the 2022 International Conference on Machine Vision, Automatic Identification and Detection (MVAID 2022)"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sun, Z., Liu, C., Qu, H., and Xie, G. (2022). PVformer: Pedestrian and vehicle detection algorithm based on Swin transformer in rainy scenes. Sensors, 22.","DOI":"10.3390\/s22155667"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109761","DOI":"10.1016\/j.patcog.2023.109761","article-title":"Learning Discriminative Feature Representation with Pixel-level Supervision for Forest Smoke Recognition","volume":"143","author":"Tao","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"10749","DOI":"10.1109\/TITS.2023.3285035","article-title":"CF-YOLO: Cross Fusion YOLO for Object Detection in Adverse Weather With a High-Quality Real Snow Dataset","volume":"24","author":"Ding","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_31","first-page":"1792","article-title":"Image-adaptive YOLO for object detection in adverse weather conditions","volume":"36","author":"Liu","year":"2022","journal-title":"Proc. Aaai Conf. Artif. Intell."},{"key":"ref_32","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv."},{"key":"ref_33","unstructured":"Kenk, M.A., and Hassaballah, M. (2020). DAWN: Vehicle detection in adverse weather nature dataset. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Petro, A.B., Sbert, C., and Morel, J.M. (2014). Multiscale retinex. Image Process. Line, 71\u201388.","DOI":"10.5201\/ipol.2014.107"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/522\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:46:37Z","timestamp":1760103997000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/522"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,14]]},"references-count":34,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24020522"],"URL":"https:\/\/doi.org\/10.3390\/s24020522","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,14]]}}}