{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T02:05:25Z","timestamp":1774317925652,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T00:00:00Z","timestamp":1726099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFF0604900"],"award-info":[{"award-number":["2022YFF0604900"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021-MS4-102"],"award-info":[{"award-number":["2021-MS4-102"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021-01-07-00-07-E00092"],"award-info":[{"award-number":["2021-01-07-00-07-E00092"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Science and Technology Project in the Transportation Industry","award":["2022YFF0604900"],"award-info":[{"award-number":["2022YFF0604900"]}]},{"name":"Key Science and Technology Project in the Transportation Industry","award":["2021-MS4-102"],"award-info":[{"award-number":["2021-MS4-102"]}]},{"name":"Key Science and Technology Project in the Transportation Industry","award":["2021-01-07-00-07-E00092"],"award-info":[{"award-number":["2021-01-07-00-07-E00092"]}]},{"name":"Innovation Program of Shanghai Municipal Education Commission","award":["2022YFF0604900"],"award-info":[{"award-number":["2022YFF0604900"]}]},{"name":"Innovation Program of Shanghai Municipal Education Commission","award":["2021-MS4-102"],"award-info":[{"award-number":["2021-MS4-102"]}]},{"name":"Innovation Program of Shanghai Municipal Education Commission","award":["2021-01-07-00-07-E00092"],"award-info":[{"award-number":["2021-01-07-00-07-E00092"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vehicle detection is essential for road traffic surveillance and active safety management. Deep learning methods have recently shown robust feature extraction capabilities and achieved improved detection results. However, vehicle detection models often perform poorly under abnormal lighting conditions, especially in highway tunnels. We proposed an adaptive vehicle detection model that accounts for varying luminance intensities to address this issue. The model categorizes the image data into abnormal and normal luminance scenarios. We employ an improved CycleGAN with edge loss as the adaptive luminance adjustment module for abnormal luminance scenarios. This module adjusts the brightness of the images to a normal level through a generative network. Finally, YOLOv7 is utilized for vehicle detection. The experimental results demonstrate that our adaptive vehicle detection model effectively detects vehicles under abnormal luminance scenarios in highway tunnels. The improved CycleGAN can effectively mitigate edge generation distortion. Under abnormal luminance scenarios, our model achieved a 16.3% improvement in precision, a 1.7% improvement in recall, and a 9.8% improvement in mAP_0.5 compared to the original YOLOv7. Additionally, our adaptive luminance adjustment module is transferable and can enhance the detection accuracy of other vehicle detection models.<\/jats:p>","DOI":"10.3390\/s24185912","type":"journal-article","created":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T05:03:04Z","timestamp":1726117384000},"page":"5912","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An Adaptive Vehicle Detection Model for Traffic Surveillance of Highway Tunnels Considering Luminance Intensity"],"prefix":"10.3390","volume":"24","author":[{"given":"Yongke","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China"},{"name":"State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China"},{"name":"Guangxi New Development Transportation Group Co., Ltd., Nanning 530029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zimu","family":"Zeng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingquan","family":"He","sequence":"additional","affiliation":[{"name":"Guangxi New Development Transportation Group Co., Ltd., Nanning 530029, China"},{"name":"School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China"},{"name":"Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0639-9134","authenticated-orcid":false,"given":"Shanchuan","family":"Yu","sequence":"additional","affiliation":[{"name":"National Engineering and Research Center for Mountainous Highways, China Merchants Chongqing Communications Research Design Institute Co., Ltd., Chongqing 400067, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuchuan","family":"Du","sequence":"additional","affiliation":[{"name":"Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1017-9118","authenticated-orcid":false,"given":"Cong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sun, C., Chen, Y., Qiu, X., Li, R., and You, L. (2024). MRD-YOLO: A Multispectral Object Detection Algorithm for Complex Road Scenes. Sensors, 24.","DOI":"10.3390\/s24103222"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"22278","DOI":"10.1109\/TITS.2021.3119079","article-title":"A novel spatio-temporal synchronization method of roadside asynchronous MMW radar-camera for sensor fusion","volume":"23","author":"Du","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104462","DOI":"10.1016\/j.trc.2023.104462","article-title":"Observer-based event-triggered adaptive platooning control for autonomous vehicles with motion uncertainties","volume":"159","author":"Xue","year":"2024","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"111903","DOI":"10.1016\/j.knosys.2024.111903","article-title":"Multi-modal trajectory forecasting with Multi-scale Interactions and Multi-pseudo-target Supervision","volume":"296","author":"Zhao","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7445","DOI":"10.1109\/TITS.2024.3351430","article-title":"A two-lane car-following model for connected vehicles under connected traffic environment","volume":"25","author":"Xue","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"04021112","DOI":"10.1061\/JTEPBS.0000640","article-title":"From Search-for-Parking to Dispatch-for-Parking in an Era of Connected and Automated Vehicles: A Macroscopic Approach","volume":"148","author":"Zhao","year":"2022","journal-title":"J. Transp. Eng. Part A Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102876","DOI":"10.1016\/j.trb.2023.102876","article-title":"Delay-throughput tradeoffs for signalized networks with finite queue capacity","volume":"180","author":"Cui","year":"2024","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dofitas, C., Gil, J.M., and Byun, Y.C. (2024). Multi-Directional Long-Term Recurrent Convolutional Network for Road Situation Recognition. Sensors, 24.","DOI":"10.3390\/s24144618"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fan, X., Xiao, D., Li, Q., and Gong, R. (2024). Snow-CLOCs: Camera-LiDAR Object Candidate Fusion for 3D Object Detection in Snowy Conditions. Sensors, 24.","DOI":"10.3390\/s24134158"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","article-title":"Object Detection with Discriminatively Trained Part-Based Models","volume":"32","author":"Felzenszwalb","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1109\/TPAMI.2006.104","article-title":"On-road vehicle detection: A review","volume":"28","author":"Sun","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1109\/TIP.2007.891147","article-title":"Vehicle Detection Using Normalized Color and Edge Map","volume":"16","author":"Tsai","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/TITS.2015.2466652","article-title":"Adaptive Sliding-Window Strategy for Vehicle Detection in Highway Environments","volume":"17","author":"Noh","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Teutsch, M., and Kruger, W. (2015, January 7\u201312). Robust and fast detection of moving vehicles in aerial videos using sliding windows. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301396"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Guo, Z., Wu, J., Tian, Y., Tang, H., and Guo, X. (2022). Real-Time Vehicle Detection Based on Improved YOLO v5. Sustainability, 14.","DOI":"10.3390\/su141912274"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ammar, A., Koubaa, A., Ahmed, M., Saad, A., and Benjdira, B. (2021). Vehicle Detection from Aerial Images Using Deep Learning: A Comparative Study. Electronics, 10.","DOI":"10.3390\/electronics10070820"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, J., Feng, Y., Shao, Y., and Liu, F. (2024). IDP-YOLOV9: Improvement of Object Detection Model in Severe Weather Scenarios from Drone Perspective. Appl. Sci., 14.","DOI":"10.3390\/app14125277"},{"key":"ref_18","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. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_19","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, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108030","DOI":"10.1016\/j.compeleceng.2022.108030","article-title":"A Lifelong Framework for Data Quality Monitoring of Roadside Sensors in Cooperative Vehicle-Infrastructure Systems","volume":"100","author":"Du","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"105098","DOI":"10.1016\/j.tust.2023.105098","article-title":"Understanding traffic bottlenecks of long freeway tunnels based on a novel location-dependent lighting-related car-following model","volume":"136","author":"Yu","year":"2023","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103787","DOI":"10.1016\/j.trc.2022.103787","article-title":"TrajGAT: A map-embedded graph attention network for real-time vehicle trajectory imputation of roadside perception","volume":"142","author":"Zhao","year":"2022","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhao, C., Ding, D., Du, Z., Shi, Y., Su, G., and Yu, S. (2023). Analysis of perception accuracy of roadside millimeter-wave radar for traffic risk assessment and early warning systems. Int. J. Environ. Res. Public Health, 20.","DOI":"10.3390\/ijerph20010879"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.patrec.2018.01.010","article-title":"LightenNet: A Convolutional Neural Network for weakly illuminated image enhancement","volume":"104","author":"Li","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ma, L., Ma, T., Liu, R., Fan, X., and Luo, Z. (2022, January 18\u201324). Toward fast, flexible, and robust low-light image enhancement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00555"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., and Cong, R. (2020, January 13\u201319). Zero-reference deep curve estimation for low-light image enhancement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2340","DOI":"10.1109\/TIP.2021.3051462","article-title":"EnlightenGAN: Deep Light Enhancement Without Paired Supervision","volume":"30","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_29","unstructured":"Han, F., Shan, Y., Cekander, R., Sawhney, H.S., and Kumar, R. (2006, January 21\u201323). A two-stage approach to people and vehicle detection with hog-based svm. Proceedings of the Performance Metrics for Intelligent Systems 2006 Workshop, Gaithersburg, MD, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.matcom.2017.12.011","article-title":"Multi-vehicle detection algorithm through combining Harr and HOG features","volume":"155","author":"Wei","year":"2019","journal-title":"Math. Comput. Simul."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, L., Xu, W., Shen, C., and Huang, Y. (2024). Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Improved HOG Features. Sensors, 24.","DOI":"10.3390\/s24051590"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3745","DOI":"10.3390\/s90503745","article-title":"Performance Analysis of the SIFT Operator for Automatic Feature Extraction and Matching in Photogrammetric Applications","volume":"9","author":"Lingua","year":"2009","journal-title":"Sensors"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"012013","DOI":"10.1088\/1742-6596\/1627\/1\/012013","article-title":"Improved hog feature vehicle recognition algorithm based on sliding window","volume":"1627","author":"Ji","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Part I 14.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Luo, E., Zeng, Z., Du, J., Chen, Z., Bai, Y., Huang, Y., and Chen, H. (2022). Quality Detection Model for Automotive Dashboard Based on an Enhanced Visual Model, SAE. Technical Report, SAE Technical Paper.","DOI":"10.4271\/2022-01-5081"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sang, J., Wu, Z., Guo, P., Hu, H., Xiang, H., Zhang, Q., and Cai, B. (2018). An Improved YOLOv2 for Vehicle Detection. Sensors, 18.","DOI":"10.3390\/s18124272"},{"key":"ref_40","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, Y., Huang, H., Xie, Q., Yao, L., and Chen, Q. (2018). Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD. Appl. Sci., 8.","DOI":"10.3390\/app8091678"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"9281","DOI":"10.1109\/TITS.2024.3374281","article-title":"Graph Matching-Based Spatiotemporal Calibration of Roadside Sensors in Cooperative Vehicle-Infrastructure Systems","volume":"25","author":"Zhao","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"124548","DOI":"10.1016\/j.eswa.2024.124548","article-title":"A two-stage framework for parking search behavior prediction through adversarial inverse reinforcement learning and transformer","volume":"255","author":"Ji","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"10953","DOI":"10.1109\/TITS.2024.3366758","article-title":"A Rapid and Convenient Spatiotemporal Calibration Method of Roadside Sensors Using Floating Connected and Automated Vehicle Data","volume":"25","author":"Zhao","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","article-title":"Generative Adversarial Networks: An Overview","volume":"35","author":"Creswell","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2019, January 15\u201320). A style-based generator architecture for generative adversarial networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3559540","article-title":"Generative adversarial networks in time series: A systematic literature review","volume":"55","author":"Brophy","year":"2023","journal-title":"Acm Comput. Surv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"130704","DOI":"10.1016\/j.conbuildmat.2023.130704","article-title":"Reconstruction of the meso-scale concrete model using a deep convolutional generative adversarial network (DCGAN)","volume":"370","author":"Liu","year":"2023","journal-title":"Constr. Build. Mater."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1109\/TITS.2019.2961679","article-title":"GAN-Based Day-to-Night Image Style Transfer for Nighttime Vehicle Detection","volume":"22","author":"Lin","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Liu, Y., Qiu, T., Wang, J., and Qi, W. (2021). A Nighttime Vehicle Detection Method with Attentive GAN for Accurate Classification and Regression. Entropy, 23.","DOI":"10.3390\/e23111490"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3326","DOI":"10.1109\/TITS.2023.3328195","article-title":"All-Day Vehicle Detection From Surveillance Videos Based on Illumination-Adjustable Generative Adversarial Network","volume":"25","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_53","first-page":"18","article-title":"Survey on skin tone detection using color spaces","volume":"2","author":"Prema","year":"2012","journal-title":"Int. J. Appl. Inf. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A computational approach to edge detection","volume":"PAMI-8","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s11263-010-0392-0","article-title":"The canny edge detector revisited","volume":"91","author":"McIlhagga","year":"2011","journal-title":"Int. J. Comput. Vis."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 17\u201324). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_57","unstructured":"Wei, C., Wang, W., Yang, W., and Liu, J. (2018). Deep retinex decomposition for low-light enhancement. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/5912\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:54:25Z","timestamp":1760111665000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/5912"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,12]]},"references-count":57,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24185912"],"URL":"https:\/\/doi.org\/10.3390\/s24185912","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,12]]}}}