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This work was supported in part by the project Percepcion Inteligente para los Vehiculos Autonomos y Conectados (InPercept) funded by Centro para el Desarrollo Tecnologico Industrial (CDTI), Spanish Ministerio de Ciencia e Innovacion, under Grant PTAS-20211011; in part by European Union, NextGenerationEU, Plan de Recuperacion, Transformacion y Resiliencia; and in part by Ministerio de Ciencia, Investigacion y Universidades (MCIU)/Agencia Estatal de Investigacion (AEI)/10.13039/501100011033 of the Spanish Government under Project PID2020-115132RB (SARAOS) and Project PID2023-148922OA-I00 (EEVOCATIONS).

Analysis of institutional authors

Fuertes, DanielAuthor

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April 12, 2025
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Enhanced Nighttime Vehicle Detection for On-Board Processing

Publicated to:Ieee Access. 13 44817-44835 - 2025-01-01 13(), DOI: 10.1109/access.2025.3548837

Authors: Encio, Leyre; Fuertes, Daniel; del-Blanco, Carlos R; Aguilar, I U; Perez-Benito, Cristina; Jevtic, Aleksandar; Jaureguizar, Fernando; Garcia, Narciso

Affiliations

Ficosa Automot SLU, Viladecavalls 08232, Spain - Author
Univ Politecn Madrid, Informat Proc & Telecommun Ctr, Grp Tratamiento Imagenes GTI, ETSI Telecomunicac, Madrid 28040, Spain - Author

Abstract

Nighttime vehicle detection poses significant challenges, particularly in scenarios with limited lighting, where visibility is often compromised. To address this problem, this paper proposes a novel nighttime vehicle detection system that dynamically adapts to extreme lighting conditions, ranging from bright daytime scenarios to challenging nighttime conditions where the vehicle's appearance may be entirely lost. For this purpose, a multi-granularity detection approach is adopted, automatically combining bounding-box and point-based representations depending on the vehicle's visibility. Bounding-box detections, reporting location and size information, are selected when the vehicle appearance is mostly visible, such as in daytime or urban nighttime scenarios with sufficient artificial street illumination. Point-based detections, indicating only location information, are used when the vehicle's appearance is not discernible, such as in rural nighttime scenarios with little or no street illumination. The system is designed as a multi-head neural network built on a shared Hourglass backbone that accepts bounding-box and point-based annotations for training and can automatically predict, depending on the scenario, vehicle bounding boxes or point-based predictions. Extensive evaluations on a combined dataset of BDD100K and PVDN demonstrate that the proposed system achieves higher detection accuracy and robustness compared to existing methods, with mean Average Precision (mAP) scores of 0.7134 on BDD100K, 0.6621 on PVDN, and 0.6814 on the combined dataset. Additionally, a self-acquired dataset, FNTVD, further enhances the evaluation by providing real-world driving conditions. The system also achieves real-time performance at 45.45 FPS, making it suitable for practical applications.

Keywords

AccuracyCamerasComputer visionConvolutional neural networksDeep learningDetectorsFeature extractionLightingNetworkNighttime detectionPerceptionRadaShapeSystemsTrainingUrban areasVehicle detectioVehicle detectionYolo

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Ieee Access due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category .

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-09-23:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 3.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 5 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 2.5.
  • The number of mentions on the social network X (formerly Twitter): 4 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/88901/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 91
  • Downloads: 24

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Encío, Leyre) and Last Author (Garcia, Narciso).

the author responsible for correspondence tasks has been Encío, Leyre.