LeftInCAR: In-Vehicle Object Detection Dataset

Published: 27 November 2025| Version 1 | DOI: 10.17632/dd4hw3r6dh.1
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Description

This dataset was developed to support research on object detection and recognition, focusing on items forgotten inside vehicles. It captures a diverse range of real-world scenarios under different lighting conditions, both indoors and outdoors, to ensure robustness and applicability in various analytical tasks. The collection contains 971 high-quality images featuring everyday objects such as: 0 - smartphone, 1 - laptop, 2 - card, 3 - suitcase, 4 - wallet, 5 - backpack, 6 - clothing, 7 - keys, 8 - glasses, 9 - handbag. The dataset is organized into two main directories (inside leftincar-data.zip): ➔ images/ – contains all visual samples. ➔ labels/ – includes YOLO-format annotation files (.txt), one per image. Images without annotations correspond to negative samples (no objects present). An additional Python script, yolo_dataset_splitter.py, is provided to automate the division of the dataset into training, validation, and testing subsets. The script ensures that all images are included in the output, creating empty label files where necessary for full YOLO compatibility.

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Institutions

Universidade do Minho Escola de Engenharia

Categories

Computer Vision, Object Detection, Deep Learning, Instance Segmentation

Funders

  • European Union under the NextGenerationEU, through a grant of the Portuguese Republic’s Recovery and Resilience Plan (PRR) Partnership Agreement, within the scope of the project BE.NEUTRAL – Agenda da Mobilidade para a neutralidade carbónica das cidades.
    Grant ID: Project ref. nr. 35 - C644874240-00000016

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