{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T18:01:20Z","timestamp":1788890480483,"version":"build-2803163510"},"reference-count":46,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CONCYTEC-PROCIENCIA","award":["PE501083603-2023"],"award-info":[{"award-number":["PE501083603-2023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The increasing use of artificial intelligence (AI) and deep learning (DL) techniques has driven advances in vehicle classification and detection applications for embedded devices with deployment constraints due to computational cost and response time. In the case of urban environments with high traffic congestion, such as the city of Lima, it is important to determine the trade-off between model accuracy, type of embedded system, and the dataset used. This study was developed using a methodology adapted from the CRISP-DM approach, which included the acquisition of traffic videos in the city of Lima, their segmentation, and manual labeling. Subsequently, three SSD-based detection models (MobileNetV1-SSD, MobileNetV2-SSD-Lite, and VGG16-SSD) were trained on the NVIDIA Jetson Orin NX 16 GB platform. The results show that the VGG16-SSD model achieved the highest average precision (mAP \u224890.7%), with a longer training time, while the MobileNetV1-SSD (512\u00d7512) model achieved comparable performance (mAP \u224890.4%) with a shorter time. Additionally, data augmentation through contrast adjustment improved the detection of minority classes such as Tuk-tuk and Motorcycle. The results indicate that, among the evaluated models, MobileNetV1-SSD (512\u00d7512) achieved the best balance between accuracy and computational load for its implementation in ADAS embedded systems in congested urban environments.<\/jats:p>","DOI":"10.3390\/jimaging12010020","type":"journal-article","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T09:56:23Z","timestamp":1767347783000},"page":"20","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Object Detection on Road: Vehicle\u2019s Detection Based on Re-Training Models on NVIDIA-Jetson Platform"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2020-1824","authenticated-orcid":false,"given":"Sleiter","family":"Ramos-Sanchez","sequence":"first","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Circuitos y Sistemas Electr\u00f3nicos de Alta Frecuencia, Universidad Nacional Tecnol\u00f3gica de Lima Sur, Villa El Salvador, Lima 15834, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2906-9741","authenticated-orcid":false,"given":"Jinmi","family":"Lezama","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Circuitos y Sistemas Electr\u00f3nicos de Alta Frecuencia, Universidad Nacional Tecnol\u00f3gica de Lima Sur, Villa El Salvador, Lima 15834, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9884-9317","authenticated-orcid":false,"given":"Ricardo","family":"Yauri","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Circuitos y Sistemas Electr\u00f3nicos de Alta Frecuencia, Universidad Nacional Tecnol\u00f3gica de Lima Sur, Villa El Salvador, Lima 15834, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9366-5177","authenticated-orcid":false,"given":"Joyce","family":"Zevallos","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Circuitos y Sistemas Electr\u00f3nicos de Alta Frecuencia, Universidad Nacional Tecnol\u00f3gica de Lima Sur, Villa El Salvador, Lima 15834, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107420","DOI":"10.1109\/ACCESS.2024.3438612","article-title":"Road Object Detection in Foggy Complex Scenes Based on Improved YOLOv8","volume":"12","author":"Cheng","year":"2024","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/ACCESS.2023.3347804","article-title":"Robust Semi-Supervised Regression for Vehicle Interior Noise Prediction","volume":"12","author":"Sim","year":"2024","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"14151","DOI":"10.1109\/ACCESS.2025.3530296","article-title":"Low-Cost Driver Monitoring System Using Deep Learning","volume":"13","author":"Khalil","year":"2025","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"25125","DOI":"10.1038\/s41598-025-08475-4","article-title":"Integrated deep learning framework for driver distraction detection and real-time road object recognition in advanced driver assistance systems","volume":"15","author":"Salakapuri","year":"2025","journal-title":"Sci. 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