{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T19:16:44Z","timestamp":1773429404064,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T00:00:00Z","timestamp":1710892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\/MCTES","award":["UIDB\/50008\/2020"],"award-info":[{"award-number":["UIDB\/50008\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Hiking and cycling have become popular activities for promoting well-being and physical activity. Portugal has been investing in hiking and cycling trail infrastructures to boost sustainable tourism. However, the lack of reliable data on the use of these trails means that the times of greatest affluence or the type of user who makes the most use of them are not recorded. These data are of the utmost importance to the managing bodies, with which they can adjust their actions to improve the management, maintenance, promotion, and use of the infrastructures for which they are responsible. The aim of this work is to present a review study on projects, techniques, and methods that can be used to identify and count the different types of users on these trails. The most promising computer vision techniques are identified and described: YOLOv3-Tiny, MobileNet-SSD V2, and FasterRCNN with ResNet-50. Their performance is evaluated and compared. The results observed can be very useful for proposing future prototypes. The challenges, future directions, and research opportunities are also discussed.<\/jats:p>","DOI":"10.3390\/fi16030104","type":"journal-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T05:56:07Z","timestamp":1710914167000},"page":"104","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Using Computer Vision to Collect Information on Cycling and Hiking Trails Users"],"prefix":"10.3390","volume":"16","author":[{"given":"Joaquim","family":"Miguel","sequence":"first","affiliation":[{"name":"Polytechnic Institute of Castelo Branco, Av. Pedro \u00c1lvares Cabral n\u00b0 12, 6000-084 Castelo Branco, Portugal"}]},{"given":"Pedro","family":"Mendon\u00e7a","sequence":"additional","affiliation":[{"name":"Polytechnic Institute of Castelo Branco, Av. Pedro \u00c1lvares Cabral n\u00b0 12, 6000-084 Castelo Branco, Portugal"}]},{"given":"Agnelo","family":"Quelhas","sequence":"additional","affiliation":[{"name":"Dire\u00e7\u00e3o Geral da Educa\u00e7\u00e3o\/ERTE, Av. 24 de Julho n.\u00ba 140-5.\u00ba piso, 1399-025 Lisboa, Portugal"},{"name":"Federa\u00e7\u00e3o Portuguesa de Ciclismo, Rua de Campolide, 237, 1070-030 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5830-3790","authenticated-orcid":false,"given":"Jo\u00e3o M. L. P.","family":"Caldeira","sequence":"additional","affiliation":[{"name":"Polytechnic Institute of Castelo Branco, Av. Pedro \u00c1lvares Cabral n\u00b0 12, 6000-084 Castelo Branco, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, Rua Marqu\u00eas d\u2019\u00c1vila e Bolama, 6201-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8057-5474","authenticated-orcid":false,"given":"Vasco N. G. J.","family":"Soares","sequence":"additional","affiliation":[{"name":"Polytechnic Institute of Castelo Branco, Av. Pedro \u00c1lvares Cabral n\u00b0 12, 6000-084 Castelo Branco, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, Rua Marqu\u00eas d\u2019\u00c1vila e Bolama, 6201-001 Covilh\u00e3, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,20]]},"reference":[{"key":"ref_1","unstructured":"(2023, December 09). Federa\u00e7\u00e3o de Campismo e Montanhismo de Portugal Regulamento de Homologa\u00e7\u00e3o De Percursos. Available online: https:\/\/cm-nisa.pt\/images\/documentos\/areas_atividade\/desporto\/regulamentopercursospedestres.pdf."},{"key":"ref_2","unstructured":"(2023, December 09). Sinaliza\u00e7\u00e3o. Available online: http:\/\/www.solasrotas.org\/2008\/09\/sinalizao.html."},{"key":"ref_3","unstructured":"Carvalho, P. (2009). Pedestrianismo e Percursos Pedestres, Cadernos de Geograia."},{"key":"ref_4","unstructured":"(2024, January 16). Federa\u00e7\u00e3o de Campismo e Montanhismo de Portugal Site Oficial Da FCMP. Available online: https:\/\/www.fcmportugal.com\/."},{"key":"ref_5","unstructured":"(2023, December 09). Federa\u00e7\u00e3o de Campismo e Montanhismo de Portugal Site Oficial Da FCMP\u2014Percursos Pedestres. Available online: https:\/\/www.fcmportugal.com\/percursos-pedestres\/."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhao, W., and Li, J. (2023, January 13\u201315). A Survey of Object Detection Methods in Inclement Weather Conditions. Proceedings of the 2023 IEEE International Conference on Unmanned Systems (ICUS), Hefei, China.","DOI":"10.1109\/ICUS58632.2023.10318342"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Vavilin, A., Lomov, A., and Roman, T. (2022, January 17\u201319). Real-Time Train Wagon Counting and Number Recognition Algorithm. Proceedings of the 2022 International Workshop on Intelligent Systems (IWIS), Ulsan, Republic of Korea.","DOI":"10.1109\/IWIS56333.2022.9920835"},{"key":"ref_8","unstructured":"(2024, February 27). Gideon Why Vision Is Better than LiDAR. Available online: https:\/\/www.gideon.ai\/resources\/why-is-vision-better-than-lidar-for-logistics-robots\/."},{"key":"ref_9","unstructured":"Cardoso, O. (2024, February 27). Vis\u00e3o Computacional: Desafios e Avan\u00e7os Recentes Na \u00c1rea|Vigeversa. Available online: https:\/\/vigeversa.com\/inteligencia-artificial\/visao-computacional\/."},{"key":"ref_10","unstructured":"Roboflow Inc (2023, November 22). Roboflow Website. Available online: https:\/\/roboflow.com\/."},{"key":"ref_11","unstructured":"(2023, November 06). Prisma PRISMA Statement. Available online: http:\/\/www.prisma-statement.org\/."},{"key":"ref_12","unstructured":"(2023, November 07). FCCN Biblioteca Do Conhecimento Online (b-On). Available online: https:\/\/www.b-on.pt\/."},{"key":"ref_13","unstructured":"ISCTE, B. (2023, November 07). Guia de Apoio Ao Utilizador (b-On); Lisboa, Portugal, 2013; Volume 3. Available online: https:\/\/www.iscte-iul.pt\/assets\/files\/2017\/01\/30\/1485777979520_Guia_b_on_MOD_SID_AU_003_4.pdf."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Minh, K.T., Dinh, Q.-V., Nguyen, T.-D., and Nhut, T.N. (2023, January 2\u20135). Vehicle Counting on Vietnamese Street. Proceedings of the 2023 IEEE Statistical Signal Processing Workshop (SSP), Hanoi, Vietnam.","DOI":"10.1109\/SSP53291.2023.10208075"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"18","DOI":"10.3991\/ijoe.v19i06.38515","article-title":"Development of Automated People Counting System Using Object Detection and Tracking","volume":"19","author":"Hong","year":"2023","journal-title":"Int. J. Online Biomed. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chatrasi, A.L.V.S.S., Batchu, A.G., Kommareddy, L.S., and Garikipati, J. (2023, January 11\u201313). Pedestrian and Object Detection Using Image Processing by YOLOv3 and YOLOv2. Proceedings of the 7th International Conference on Trends in Electronics and Informatics, ICOEI 2023\u2014Proceedings, Tirunelveli, India.","DOI":"10.1109\/ICOEI56765.2023.10125788"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Anil, J.M., Mathews, L., Renji, R., Jose, R.M., and Thomas, S. (2023, January 17\u201319). Vehicle Counting Based on Convolution Neural Network. Proceedings of the 7th International Conference on Intelligent Computing and Control Systems, ICICCS, Madurai, India.","DOI":"10.1109\/ICICCS56967.2023.10142302"},{"key":"ref_18","unstructured":"Myint, E.P., and Sein, M.M. (2021). LifeTech 2021, Proceedings of the 2021 IEEE 3rd Global Conference on Life Sciences and Technologies, Nara, Japan, 9\u201311 March 2021, IEEE."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104268","DOI":"10.1016\/j.imavis.2023.104628","article-title":"Intelligent Multimodal Pedestrian Detection Using Hybrid Metaheuristic Optimization with Deep Learning Model","volume":"131","author":"Kolluri","year":"2023","journal-title":"Image Vis. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mimboro, P., Heryadi, Y., Suparta, W., and Wibowo, A. (2021, January 8\u20139). Realtime Vehicle Counting Method Using Haar Cascade Classifier Model. Proceedings of the 2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA, Surabaya, Indonesia.","DOI":"10.1109\/ICAMIMIA54022.2021.9807721"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vignarca, D., Prakash, J., Vignati, M., and Sabbioni, E. (2021, January 17\u201319). Improved Person Counting Performance Using Kalman Filter Based on Image Detection and Tracking. Proceedings of the 2021 AEIT International Conference on Electrical and Electronic Technologies for Automotive, AEIT AUTOMOTIVE, Torino, Italy.","DOI":"10.23919\/AEITAUTOMOTIVE52815.2021.9662745"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Minh, H.T., Mai, L., and Minh, T.V. (2021, January 24\u201326). Performance Evaluation of Deep Learning Models on Embedded Platform for Edge AI-Based Real Time Traffic Tracking and Detecting Applications. Proceedings of the 2021 15th International Conference on Advanced Computing and Applications, ACOMP, Ho Chi Minh City, Vietnam.","DOI":"10.1109\/ACOMP53746.2021.00024"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2689","DOI":"10.3390\/s22072689","article-title":"EmbeddedPigCount: Pig Counting with Video Object Detection and Tracking on an Embedded Board","volume":"22","author":"Kim","year":"2022","journal-title":"Sensors"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8816","DOI":"10.3390\/en15238816","article-title":"Counting People and Bicycles in Real Time Using YOLO on Jetson Nano","volume":"15","author":"Gomes","year":"2022","journal-title":"Energies"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Morera, \u00c1., S\u00e1nchez, \u00c1., Moreno, A.B., Sappa, \u00c1.D., and V\u00e9lez, J.F. (2020). SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities. Sensors, 20.","DOI":"10.3390\/s20164587"},{"key":"ref_27","unstructured":"(2023, October 30). Amazon Web Services O Que \u00e9 Vis\u00e3o Computacional?\u2014Explica\u00e7\u00e3o de IA\/ML de Reconhecimento de Imagem\u2014AWS. Available online: https:\/\/aws.amazon.com\/pt\/what-is\/computer-vision\/."},{"key":"ref_28","unstructured":"(2023, October 17). IBM O Que S\u00e3o Redes Neurais? IBM. Available online: https:\/\/www.ibm.com\/br-pt\/topics\/neural-networks."},{"key":"ref_29","unstructured":"Nunes dos Santos, V. (2018). Reconhecimento de Objetos Em Uma Cena Utilizando Redes Neurais Convolucionais. [Bachelor\u2019s Thesis, Universidade Tecnol\u00f3gica Federal do Paran\u00e1]."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., and Ghayvat, H. (2021). CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope. Electronics, 10.","DOI":"10.3390\/electronics10202470"},{"key":"ref_31","unstructured":"(2023, November 05). Deep Learning Book Cap\u00edtulo 43\u2014Camadas de Pooling Em Redes Neurais Convolucionais. Available online: https:\/\/www.deeplearningbook.com.br\/camadas-de-pooling-em-redes-neurais-convolucionais\/."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Barbosa, G., Bezerra, G.M., de Medeiros, D.S., Andreoni Lopez, M., and Mattos, D. (2021, January 4\u20137). Seguran\u00e7a Em Redes 5G: Oportunidades e Desafios Em Detec\u00e7\u00e3o de Anomalias e Predi\u00e7\u00e3o de Tr\u00e1fego Baseadas Em Aprendizado de M\u00e1quina. Proceedings of the Minicursos do XXI Simp\u00f3sio Brasileiro de Seguran\u00e7a da Informa\u00e7\u00e3o e de Sistemas Computacionais, Online, Bel\u00e9m.","DOI":"10.5753\/sbc.7165.8.4"},{"key":"ref_33","unstructured":"Poloni, K. (2023, November 01). Redes Neurais Convolucionais. Available online: https:\/\/medium.com\/itau-data\/redes-neurais-convolucionais-2206a089c715."},{"key":"ref_34","unstructured":"Archana, V., Kalaiselvi, S., Thamaraiselvi, D., Gomathi, V., and Sowmiya, R. (2022, January 13\u201315). A Novel Object Detection Framework Using Convolutional Neural Networks (CNN) and RetinaNet. Proceedings of the International Conference on Automation, Computing and Renewable Systems, ICACRS, Pudukkottai, India."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Carranza-Garc\u00eda, M., Torres-Mateo, J., Lara-Ben\u00edtez, P., and Garc\u00eda-Guti\u00e9rrez, J. (2020). On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data. Remote Sens., 13.","DOI":"10.3390\/rs13010089"},{"key":"ref_36","unstructured":"Zhou, L., Lin, T., and Knoll, A. (2023). Fast and Accurate Object Detection on Asymmetrical Receptive Field. Comput. Vis. Pattern Recognit. Arxiv."},{"key":"ref_37","unstructured":"Thakur, N. (2023, December 23). A Detailed Introduction to Two Stage Object Detectors. Available online: https:\/\/namrata-thakur893.medium.com\/a-detailed-introduction-to-two-stage-object-detectors-d4ba0c06b14e."},{"key":"ref_38","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. Comput. Vis. Pattern Recognit."},{"key":"ref_39","unstructured":"Bajaj, V. (2024, January 28). The YOLO Algorithm\u2014Deep Learning Specialization\u2014Coursera. Available online: https:\/\/vikram-bajaj.gitbook.io\/deep-learning-specialization-coursera\/convolutional-neural-networks\/object-detection\/the-yolo-algorithm."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Adarsh, P., Rathi, P., and Kumar, M. (2020, January 6\u20137). YOLO V3-Tiny: Object Detection and Recognition Using Stage Improved Model. Proceedings of the 2020 6th International Conference on Advanced Computing & Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS48705.2020.9074315"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"142931","DOI":"10.1109\/ACCESS.2020.3013934","article-title":"A Systematic Study of Tiny YOLO3 Inference: Toward Compact Brainware Processor With Less Memory and Logic Gate","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_42","unstructured":"Cochard, D. (2024, January 16). MobilenetSSD: A Machine Learning Model for Fast Object Detection. Available online: https:\/\/medium.com\/axinc-ai\/mobilenetssd-a-machine-learning-model-for-fast-object-detection-37352ce6da7d."},{"key":"ref_43","first-page":"136","article-title":"Object Detection Using YOLO And Mobilenet SSD: A Comparative Study","volume":"11","author":"Sabina","year":"2022","journal-title":"Int. J. Eng. Res. Technol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (2019). Searching for MobileNetV3. Comput. Vis. Pattern Recognit.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_45","unstructured":"Sovit Rath, R. (2024, February 26). Object Detection Using PyTorch Faster RCNN ResNet50 FPN V2. Available online: https:\/\/debuggercafe.com\/object-detection-using-pytorch-faster-rcnn-resnet50-fpn-v2\/."},{"key":"ref_46","unstructured":"(2024, February 26). ResNet-50: The Basics and a Quick Tutorial. Available online: https:\/\/datagen.tech\/guides\/computer-vision\/resnet-50\/."},{"key":"ref_47","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_48","unstructured":"(2023, November 29). Pixabay Pixabay Website. Available online: https:\/\/pixabay.com\/."},{"key":"ref_49","unstructured":"(2023, November 29). Unsplash Unsplash Website. Available online: https:\/\/unsplash.com\/pt-br."},{"key":"ref_50","unstructured":"Miguel, J., and Mendon\u00e7a, P. (2024, January 26). Person, Bicycle and Motorcyle Dataset. Available online: https:\/\/universe.roboflow.com\/projeto-gfvuy\/person-bicycle-motorcyle\/model\/7."},{"key":"ref_51","unstructured":"Nelson, J. (2024, January 15). How to Label Image Data for Computer Vision Models. Available online: https:\/\/blog.roboflow.com\/tips-for-how-to-label-images\/."},{"key":"ref_52","unstructured":"(2023, December 22). Google Colab. Available online: https:\/\/colab.google\/."},{"key":"ref_53","unstructured":"(2023, December 23). Weka Why GPUs for Machine Learning? A Complete Explanation\u2014WEKA. Available online: https:\/\/www.weka.io\/learn\/ai-ml\/gpus-for-machine-learning\/."},{"key":"ref_54","unstructured":"(2024, January 15). Google Colab\u2014FAQ. Available online: https:\/\/research.google.com\/colaboratory\/faq.html#resource-limits."},{"key":"ref_55","unstructured":"(2023, December 23). Armazenamento Na Nuvem Pessoal e Plataforma de Partilha de Ficheiros\u2014Google. Available online: https:\/\/www.google.com\/intl\/pt-PT\/drive\/."},{"key":"ref_56","unstructured":"(2023, December 23). Roboflow Notebook\u2014Train Yolov4 Tiny Object Detection On Custom Data. Available online: https:\/\/github.com\/roboflow\/notebooks\/blob\/main\/notebooks\/train-yolov4-tiny-object-detection-on-custom-data.ipynb."},{"key":"ref_57","unstructured":"NVIDIA (2023, December 23). NVIDIA CUDA Compiler Driver. Available online: https:\/\/docs.nvidia.com\/cuda\/cuda-compiler-driver-nvcc\/index.html."},{"key":"ref_58","unstructured":"(2023, December 23). Darknet Darknet: Open Source Neural Networks in C. Available online: https:\/\/pjreddie.com\/darknet\/."},{"key":"ref_59","unstructured":"(2023, December 23). yaming116 Darknet\u2014Yolov3-Tiny Weights. Available online: https:\/\/github.com\/smarthomefans\/darknet-test\/blob\/master\/yolov3-tiny.weights."},{"key":"ref_60","unstructured":"Traore, M. (2023, December 23). Roboflow\u2019s Python Pip Package For Computer Vision. Available online: https:\/\/blog.roboflow.com\/pip-install-roboflow\/."},{"key":"ref_61","unstructured":"Charette, S. (2024, January 28). Programming Comments\u2014Darknet FAQ. Available online: https:\/\/www.ccoderun.ca\/programming\/darknet_faq\/."},{"key":"ref_62","unstructured":"Juras, E., and Technology Consultants, E. (2024, January 15). Notebook\u2014Train TFLite2 Object Detection Model. Available online: https:\/\/colab.research.google.com\/github\/EdjeElectronics\/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi\/blob\/master\/Train_TFLite2_Object_Detction_Model.ipynb."},{"key":"ref_63","unstructured":"Juras, E., Technology Consultants, E., Miguel, J., and Mendon\u00e7a, P. (2024, January 15). Notebook Adaptado\u2014Train TFLite2 Object Detection Model. Available online: https:\/\/colab.research.google.com\/drive\/1rK3GNbJA_i_rupahuWyWgvrEHHvvp44i?authuser=1#scrollTo=fF8ysCfYKgTP."},{"key":"ref_64","unstructured":"(2024, January 15). TensorFlow; vighneshbirodkar; TF Object Detection Team TensorFlow 2 Detection Model Zoo. Available online: https:\/\/github.com\/tensorflow\/models\/blob\/master\/research\/object_detection\/g3doc\/tf2_detection_zoo.md."},{"key":"ref_65","unstructured":"(2024, January 15). sovit-123 FasterRCNN Pytorch Training Pipeline: PyTorch Faster R-CNN Object Detection on Custom Dataset. Available online: https:\/\/github.com\/sovit-123\/fasterrcnn-pytorch-training-pipeline."},{"key":"ref_66","unstructured":"(2023, December 24). PyTorch PyTorch. Available online: https:\/\/pytorch.org\/."},{"key":"ref_67","unstructured":"(2024, January 17). Lakera Average Precision. Available online: https:\/\/www.lakera.ai\/ml-glossary\/average-precision."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.1002\/bjs.10244","article-title":"Overfitting","volume":"103","author":"Cook","year":"2016","journal-title":"Br. J. Surg."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"022022","DOI":"10.1088\/1742-6596\/1168\/2\/022022","article-title":"An Overview of Overfitting and Its Solutions","volume":"1168","author":"Ying","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_70","unstructured":"(2024, January 16). TensorFlow TensorBoard. Available online: https:\/\/www.tensorflow.org\/tensorboard?hl=pt-br."},{"key":"ref_71","unstructured":"Rosebrock, A. (2024, January 17). YOLO and Tiny-YOLO Object Detection on the Raspberry Pi and Movidius NCS\u2014PyImageSearch. Available online: https:\/\/pyimagesearch.com\/2020\/01\/27\/yolo-and-tiny-yolo-object-detection-on-the-raspberry-pi-and-movidius-ncs\/."},{"key":"ref_72","unstructured":"Serra, R. (2023, November 29). Como Funcionam Os Pain\u00e9is Solares Para Casa?. Available online: https:\/\/www.doutorfinancas.pt\/energia\/como-funcionam-os-paineis-solares-para-casa\/."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/16\/3\/104\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:16:32Z","timestamp":1760105792000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/16\/3\/104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,20]]},"references-count":72,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["fi16030104"],"URL":"https:\/\/doi.org\/10.3390\/fi16030104","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,20]]}}}