{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T15:23:24Z","timestamp":1769181804518,"version":"3.49.0"},"reference-count":59,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T00:00:00Z","timestamp":1617148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection.<\/jats:p>","DOI":"10.3390\/a14040114","type":"journal-article","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T10:24:33Z","timestamp":1617186273000},"page":"114","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":167,"title":["Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5"],"prefix":"10.3390","volume":"14","author":[{"given":"Margrit","family":"Kasper-Eulaers","sequence":"first","affiliation":[{"name":"Capia AS, 9008 Troms\u00f8, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3061-4057","authenticated-orcid":false,"given":"Nico","family":"Hahn","sequence":"additional","affiliation":[{"name":"Capia AS, 9008 Troms\u00f8, Norway"}]},{"given":"Stian","family":"Berger","sequence":"additional","affiliation":[{"name":"Capia AS, 9008 Troms\u00f8, Norway"}]},{"given":"Tom","family":"Sebulonsen","sequence":"additional","affiliation":[{"name":"Capia AS, 9008 Troms\u00f8, Norway"}]},{"given":"\u00d8ystein","family":"Myrland","sequence":"additional","affiliation":[{"name":"Capia AS, 9008 Troms\u00f8, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1007-0945","authenticated-orcid":false,"given":"Per Egil","family":"Kummervold","sequence":"additional","affiliation":[{"name":"Capia AS, 9008 Troms\u00f8, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, J., Huang, M., Jin, X., and Li, X. (2017). A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2. Algorithms, 10.","DOI":"10.3390\/a10040127"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"114481","DOI":"10.1016\/j.eswa.2020.114481","article-title":"DeepThin: A novel lightweight CNN architecture for traffic sign recognition without GPU requirements","volume":"168","author":"Haque","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, B., Wang, G., Wang, H., Xu, C., Li, Y., and Xu, L. (2021). Detecting Small Chinese Traffic Signs via Improved YOLOv3 Method. Math. Probl. Eng., 2021.","DOI":"10.1155\/2021\/8826593"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhou, K., Zhan, Y., and Fu, D. (2021). Learning Region-Based Attention Network for Traffic Sign Recognition. Sensors, 21.","DOI":"10.3390\/s21030686"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sun, C., Ai, Y., Wang, S., and Zhang, W. (2020). Dense-RefineDet for Traffic Sign Detection and Classification. Sensors, 20.","DOI":"10.3390\/s20226570"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1049\/iet-its.2019.0475","article-title":"Improved detection method for traffic signs in real scenes applied in intelligent and connected vehicles","volume":"14","author":"Du","year":"2020","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.isprsjprs.2020.10.003","article-title":"Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation","volume":"171","author":"Yazdan","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nodado, J.T.G., Morales, H.C.P., Abugan, M.A.P., Olisea, J.L., Aralar, A.C., and Loresco, P.J.M. (2018, January 28\u201331). Intelligent Traffic Light System Using Computer Vision with Android Monitoring and Control. Proceedings of the TENCON 2018\u20142018 IEEE Region 10 Conference, Jeju, Korea.","DOI":"10.1109\/TENCON.2018.8650084"},{"key":"ref_9","unstructured":"Poddar, M., Giridhar, M.K., Prabhu, A.S., and Umadevi, V. (2016, January 18\u201319). Automated traffic monitoring system using computer vision. Proceedings of the 2016 International Conference on ICT in Business Industry & Government (ICTBIG), Indore, India."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Loce, R.P., Bala, R., and Trivedi, M. (2017). Detection of Moving Violations. Computer Vision and Imaging in Intelligent Transportation Systems, Wiley-IEEE Press. Chapter 5.","DOI":"10.1002\/9781118971666"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s00607-020-00869-8","article-title":"An improved YOLO-based road traffic monitoring system","volume":"103","author":"Abbasi","year":"2021","journal-title":"Computing"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xu, T., Zhang, Z., Wu, X., Qi, L., and Han, Y. (2021). Recognition of lane-changing behaviour with machine learning methods at freeway off-ramps. Phys. A Stat. Mech. Appl., 567.","DOI":"10.1016\/j.physa.2020.125691"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s12544-008-0002-1","article-title":"Towards automatic near real-time traffic monitoring with an airborne wide angle camera system","volume":"1","author":"Rosenbaum","year":"2009","journal-title":"Eur. Transp. Res. Rev."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhu, E., Xu, M., and Pi, D.C. (2021). Vehicle Type Recognition Algorithm Based on Improved Network in Network. Complexity, 2021.","DOI":"10.1155\/2021\/6061939"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"14265","DOI":"10.1109\/ACCESS.2019.2963486","article-title":"Vehicle Type Classification Using an Enhanced Sparse-Filtered Convolutional Neural Network With Layer-Skipping Strategy","volume":"8","author":"Awang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1049\/iet-its.2018.5316","article-title":"Vehicle classification approach based on the combined texture and shape features with a compressive DL","volume":"13","author":"Sun","year":"2019","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kang, Q., Zhao, H., Yang, D., Ahmed, H.S., and Ma, J. (2020). Lightweight convolutional neural network for vehicle recognition in thermal infrared images. Infrared Phys. Technol., 104.","DOI":"10.1016\/j.infrared.2019.103120"},{"key":"ref_18","first-page":"2489","article-title":"A Two-Stage Vehicle Type Recognition Method Combining the Most Effective Gabor Features","volume":"65","author":"Sun","year":"2020","journal-title":"CMC-Comput. Mater. Contin."},{"key":"ref_19","unstructured":"Uus, J., and Krilavi\u010dius, T. (2021, March 28). Detection of Different Types of Vehicles from Aerial Imagery. Available online: https:\/\/www.vdu.lt\/cris\/handle\/20.500.12259\/102060."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"113","DOI":"10.3141\/2645-13","article-title":"Automated Vehicle Recognition with Deep Convolutional Neural Networks","volume":"2645","author":"Asare","year":"2017","journal-title":"Transp. Res. Rec."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huttunen, H., Yancheshmeh, F.S., and Chen, K. (2016, January 19\u201322). Car type recognition with Deep Neural Networks. Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden.","DOI":"10.1109\/IVS.2016.7535529"},{"key":"ref_22","unstructured":"Zhou, Y., and Cheung, N.M. (2016). Vehicle classification using transferable deep neural network features. arXiv."},{"key":"ref_23","first-page":"277","article-title":"Vehicle Type Classification with Geometric and Appearance Attributes","volume":"8","author":"Moussa","year":"2014","journal-title":"World Acad. Sci. Eng. Technol. Int. J. Civ. Environ. Struct. Constr. Archit. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.jvlc.2014.02.001","article-title":"Shadow Elimination and Vehicles Classification Approaches in Traffic Video Surveillance Context","volume":"25","author":"Asaidi","year":"2014","journal-title":"J. Vis. Lang. Comput."},{"key":"ref_25","unstructured":"Han, D., Leotta, M.J., Cooper, D.B., and Mundy, J.L. (2005, January 15\u201316). Vehicle Class Recognition from Video-Based on 3D Curve Probes. Proceedings of the 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, Beijing, China."},{"key":"ref_26","first-page":"2","article-title":"A Generic Deformable Model for Vehicle Recognition","volume":"Volume 1","author":"Ferryman","year":"1995","journal-title":"BMVC"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"462","DOI":"10.29207\/resti.v4i3.1871","article-title":"A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model","volume":"4","author":"Fachrie","year":"2020","journal-title":"Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi)"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Song, H., Liang, H., Li, H., Dai, Z., and Yun, X. (2019). Vision-based vehicle detection and counting system using deep learning in highway scenes. Eur. Transp. Res. Rev., 11.","DOI":"10.1186\/s12544-019-0390-4"},{"key":"ref_29","first-page":"2062","article-title":"RT-VC: An Efficient Real-Time Vehicle Counting Approach","volume":"97","author":"Alghyaline","year":"2019","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"ref_30","unstructured":"Huang, D.S., Jo, K.H., and Wang, L. (2014). Computer Vision Based Traffic Monitoring System for Multi-track Freeways. Intelligent Computing Methodologies, Springer International Publishing."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kun, A.J., and Vamossy, Z. (2009, January 30\u201331). Traffic monitoring with computer vision. Proceedings of the 2009 7th International Symposium on Applied Machine Intelligence and Informatics, Herlany, Slovakia.","DOI":"10.1109\/SAMI.2009.4956624"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Jiang, S., Jiang, H., Ma, S., and Jiang, Z. (2020). Detection of Parking Slots Based on Mask R-CNN. Appl. Sci., 10.","DOI":"10.3390\/app10124295"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kim, S., Kim, J., Ra, M., and Kim, W.Y. (2020). Vacant Parking Slot Recognition Method for Practical Autonomous Valet Parking System Using around View Image. Symmetry, 12.","DOI":"10.3390\/sym12101725"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, C., and Du, B. (2020). Image-Based Approach for Parking-Spot Detection with Occlusion Handling. J. Transp. Eng. Part Syst., 146.","DOI":"10.1061\/JTEPBS.0000420"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"T\u0103tulea, P., C\u0103lin, F., Brad, R., Br\u00e2ncovean, L., and Greavu, M. (2019). An Image Feature-Based Method for Parking Lot Occupancy. Future Internet, 11.","DOI":"10.3390\/fi11080169"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7693","DOI":"10.1109\/JIOT.2019.2902887","article-title":"Deep Learning-Based Video System for Accurate and Real-Time Parking Measurement","volume":"6","author":"Cai","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"012084","DOI":"10.1088\/1742-6596\/1325\/1\/012084","article-title":"Vehicle and Parking Space Detection Based on Improved YOLO Network Model","volume":"1325","author":"Ding","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_38","unstructured":"Acharya, D., Yan, W., and Khoshelham, K. (2021, March 28). Real-Time Image-Based Parking OCCUPANCY detection Using Deep Learning. Research@ Locate. Available online: https:\/\/www.researchgate.net\/publication\/323796590."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Amato, G., Carrara, F., Falchi, F., Gennaro, C., Meghini, C., and Vairo, C. (2016). Deep Learning for Decentralized Parking Lot Occupancy Detection. Expert Syst. Appl., 72.","DOI":"10.1016\/j.eswa.2016.10.055"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1049\/iet-its.2014.0271","article-title":"Trajectory analysis for parking lot vacancy detection system","volume":"10","author":"Masmoudi","year":"2016","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Valipour, S., Siam, M., Stroulia, E., and Jagersand, M. (2016, January 12\u201314). Parking-stall vacancy indicator system, based on deep convolutional neural networks. Proceedings of the 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), Reston, VA, USA.","DOI":"10.1109\/WF-IoT.2016.7845408"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Men\u00e9ndez, J.M., Postigo, C., and Torres, J. (2015). Vacant parking area estimation through background subtraction and transience map analysis. IET Intell. Transp. Syst., 9.","DOI":"10.1049\/iet-its.2014.0090"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"De Almeida, P.R., Oliveira, L.S., Britto, A.S., Silva, E.J., and Koerich, A.L. (2015). PKLot\u2014A Robust Dataset for Parking Lot Classification. Expert Syst. Appl., 42.","DOI":"10.1016\/j.eswa.2015.02.009"},{"key":"ref_44","first-page":"33","article-title":"One-Day Long Statistical Analysis of Parking Demand by Using Single-Camera Vacancy Detection","volume":"14","author":"Jermsurawong","year":"2014","journal-title":"J. Transp. Syst. Eng. Inf. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Fabian, T. (2013, January 29\u201331). A Vision-Based Algorithm for Parking Lot Utilization Evaluation Using Conditional Random Fields. Proceedings of the International Symposium on Visual Computing, Crete, Greece.","DOI":"10.1007\/978-3-642-41939-3_22"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1598","DOI":"10.1109\/TCSVT.2013.2254961","article-title":"Vacant Parking Space Detection Based on Plane-Based Bayesian Hierarchical Framework","volume":"23","author":"Huang","year":"2013","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ichihashi, H., Notsu, A., Honda, K., Katada, T., and Fujiyoshi, M. (2009, January 20\u201324). Vacant parking space detector for outdoor parking lot by using surveillance camera and FCM classifier. Proceedings of the 2009 IEEE International Conference on Fuzzy Systems, Jeju, Korea.","DOI":"10.1109\/FUZZY.2009.5277099"},{"key":"ref_48","first-page":"1","article-title":"Integrated Approach in the Design of Car Park Occupancy Information System (COINS)","volume":"35","author":"Bong","year":"2008","journal-title":"IAENG Int. J. Comput. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Funck, S., Mohler, N., and Oertel, W. (2004, January 14\u201317). Determining car-park occupancy from single images. Proceedings of the IEEE Intelligent Vehicles Symposium, Parma, Italy.","DOI":"10.1109\/IVS.2004.1336403"},{"key":"ref_50","unstructured":"Jocher, G., Stoken, A., Borovec, J., NanoCode012, ChristopherSTAN, Changyu, L., Laughing, tkianai, yxNONG, and Hogan, A. (2021, March 28). Ultralytics\/yolov5: v4.0\u2014nn.SiLU() Activations, Weights & Biases Logging, PyTorch Hub Integration. Available online: https:\/\/doi.org\/10.5281\/zenodo.4418161."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2021, February 03). You Only Look Once: Unified, Real-Time Object Detection. Available online: https:\/\/doi.org\/10.1109\/CVPR.2016.91.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_52","unstructured":"Joseph, R., Santosh, D., Ross, G., and Ali, F. (2021, March 03). YOLO: Real-Time Object Detection. Available online: https:\/\/pjreddie.com\/darknet\/yolo\/."},{"key":"ref_53","unstructured":"Benjdira, B., Khursheed, T., Koubaa, A., Ammar, A., and Ouni, K. (2021, February 03). Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3, Available online: http:\/\/xxx.lanl.gov\/abs\/1812.10968."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"052018","DOI":"10.1088\/1757-899X\/569\/5\/052018","article-title":"Vehicle target detection in complex scenes based on YOLOv3 algorithm","volume":"569","author":"Ouyang","year":"2019","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Lin, T., Maire, M., Belongie, S.J., Bourdev, L.D., Girshick, R.B., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014). Microsoft COCO: Common Objects in Context. CoRR, Available online: http:\/\/xxx.lanl.gov\/abs\/1405.0312.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_56","unstructured":"Fabrice, B. (2021, February 03). FFmpeg. Available online: https:\/\/ffmpeg.org\/."},{"key":"ref_57","unstructured":"Fabrice, B. (2021, February 03). FFmpeg Filters Documentation Select, Aselect. Available online: https:\/\/ffmpeg.org\/ffmpeg-filters.html#select_002c-aselect."},{"key":"ref_58","unstructured":"Roboflow (2021, February 03). How to Train YOLOv5 on Custom Objects. Available online: https:\/\/colab.research.google.com\/drive\/1gDZ2xcTOgR39tGGs-EZ6i3RTs16wmzZQ."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zoph, B., Cubuk, E.D., Ghiasi, G., Lin, T.Y., Shlens, J., and Le, Q.V. (2019). Learning Data Augmentation Strategies for Object Detection. arXiv.","DOI":"10.1109\/CVPR.2019.00020"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/4\/114\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:10:45Z","timestamp":1760364645000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/4\/114"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,31]]},"references-count":59,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["a14040114"],"URL":"https:\/\/doi.org\/10.3390\/a14040114","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,31]]}}}