Real-Time Detection of Road Anomalies for Integration in Rider Assistance Systems

Tiago Silva; João Silva; António Sousa; Vítor Filipe

doi:10.62762/TIS.2025.418469

Volume 3, Issue 1, ICCK Transactions on Intelligent Systematics

Volume 3, Issue 1, 2026

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ICCK Transactions on Intelligent Systematics, Volume 3, Issue 1, 2026: 32-54

Free to Read | Research Article | 03 March 2026

Real-Time Detection of Road Anomalies for Integration in Rider Assistance Systems

Tiago Silva 1 *

João Silva 1 *

António Sousa 1,2 *

Vítor Filipe 1,2 *

1 University of Trás-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal

2 INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portuga

* Corresponding Authors: Tiago Silva, [email protected] ; João Silva, [email protected] ; António Sousa, [email protected] ; Vítor Filipe, [email protected]

DOI: 10.62762/TIS.2025.418469

ARK: ark:/57805/tis.2025.418469

Received: 29 September 2025, Accepted: 19 November 2025, Published: 03 March 2026

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Abstract

Road safety has become an increasingly important concern and the integration of Advanced Rider Assistance Systems and Advanced Driver Assistance Systems plays a crucial role in preventing accidents. This work proposes a computer vision pipeline to automatically detect hazardous road anomalies—loose gravel, potholes, and puddles—from a motorcycle-mounted camera, targeting real-time operation on embedded edge devices. A hybrid dataset of 28764 annotated images was created by combining real-world photos, Blender-rendered synthetic scenes, and AI-generated images to improve diversity and coverage. Multiple state-of-the-art object detectors were trained and benchmarked, including the YOLOv5/7/11/12 families and the transformer-based RT-DETR architecture. While the RT-DETR model achieved the highest precision overall, its computational complexity and heavy resource requirements limited its suitability for real-time deployment on low-cost embedded platforms. Conversely, the YOLOv11n model demonstrated the best accuracy–efficiency trade-off, reaching [email protected] = 0.872 at 320$\times$320 with 0.045 s/frame on a Jetson Nano, while lighter variants remained viable on Raspberry Pi boards. Across classes, gravel was the most reliably detected, and operating points around a confidence threshold of $\tau \approx 0.31$ yielded balanced F1 scores up to 0.82. Although results show that automatic road-condition monitoring on affordable hardware is feasible, the prototype has not yet undergone on-road field trials. It does not include an integrated rider alert module or energy-use assessment. These gaps define the immediate roadmap for deployment.

Graphical Abstract

Real-Time Detection of Road Anomalies for Integration in Rider Assistance Systems

Keywords

road monitoring

computer vision

object detection

edge devices

advanced rider assistance systems

Data Availability Statement

Data will be made available on request.

Funding

The study was developed under the project A-MoVeR – “Mobilizing Agenda for the Development of Products & Systems towards an Intelligent and Green Mobility”, operation n.º 02/C05-i01.01/2022.PC646908627-00000069, approved under the terms of the call n.º 02/C05-i01/2022 – Mobilizing Agendas for Business Innovation, financed by European funds provided to Portugal by the Recovery and Resilience Plan (RRP), in the scope of the European Recovery and Resilience Facility (RRF), framed in the Next Generation UE, for the period from 2021-2026.

Conflicts of Interest

The authors declare no conflicts of interest.

AI Use Statement

The authors declare that no generative AI was used in the preparation of this manuscript.

Ethical Approval and Consent to Participate

Not applicable.

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Silva, T., Silva, J., Sousa, A., & Filipe, V. (2026). Real-Time Detection of RoadAnomaliesforIntegration in Rider Assistance Systems. ICCK Transactions on Intelligent Systematics, 3(1), 32–54. https://doi.org/10.62762/TIS.2025.418469

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TY  - JOUR
AU  - Silva, Tiago
AU  - Silva, João
AU  - Sousa, António
AU  - Filipe, Vítor
PY  - 2026
DA  - 2026/03/03
TI  - Real-Time Detection of Road Anomalies for Integration in Rider Assistance Systems
JO  - ICCK Transactions on Intelligent Systematics
T2  - ICCK Transactions on Intelligent Systematics
JF  - ICCK Transactions on Intelligent Systematics
VL  - 3
IS  - 1
SP  - 32
EP  - 54
DO  - 10.62762/TIS.2025.418469
UR  - https://www.icck.org/article/abs/TIS.2025.418469
KW  - road monitoring
KW  - computer vision
KW  - object detection
KW  - edge devices
KW  - advanced rider assistance systems
AB  - Road safety has become an increasingly important concern and the integration of Advanced Rider Assistance Systems and Advanced Driver Assistance Systems plays a crucial role in preventing accidents. This work proposes a computer vision pipeline to automatically detect hazardous road anomalies—loose gravel, potholes, and puddles—from a motorcycle-mounted camera, targeting real-time operation on embedded edge devices. A hybrid dataset of 28764 annotated images was created by combining real-world photos, Blender-rendered synthetic scenes, and AI-generated images to improve diversity and coverage. Multiple state-of-the-art object detectors were trained and benchmarked, including the YOLOv5/7/11/12 families and the transformer-based RT-DETR architecture. While the RT-DETR model achieved the highest precision overall, its computational complexity and heavy resource requirements limited its suitability for real-time deployment on low-cost embedded platforms. Conversely, the YOLOv11n model demonstrated the best accuracy–efficiency trade-off, reaching [email protected] = 0.872 at 320$\times$320 with 0.045 s/frame on a Jetson Nano, while lighter variants remained viable on Raspberry Pi boards. Across classes, gravel was the most reliably detected, and operating points around a confidence threshold of $\tau \approx 0.31$ yielded balanced F1 scores up to 0.82. Although results show that automatic road-condition monitoring on affordable hardware is feasible, the prototype has not yet undergone on-road field trials. It does not include an integrated rider alert module or energy-use assessment. These gaps define the immediate roadmap for deployment.
SN  - 3068-5079
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Silva2026RealTime,
  author = {Tiago Silva and João Silva and António Sousa and Vítor Filipe},
  title = {Real-Time Detection of Road Anomalies for Integration in Rider Assistance Systems},
  journal = {ICCK Transactions on Intelligent Systematics},
  year = {2026},
  volume = {3},
  number = {1},
  pages = {32-54},
  doi = {10.62762/TIS.2025.418469},
  url = {https://www.icck.org/article/abs/TIS.2025.418469},
  abstract = {Road safety has become an increasingly important concern and the integration of Advanced Rider Assistance Systems and Advanced Driver Assistance Systems plays a crucial role in preventing accidents. This work proposes a computer vision pipeline to automatically detect hazardous road anomalies—loose gravel, potholes, and puddles—from a motorcycle-mounted camera, targeting real-time operation on embedded edge devices. A hybrid dataset of 28764 annotated images was created by combining real-world photos, Blender-rendered synthetic scenes, and AI-generated images to improve diversity and coverage. Multiple state-of-the-art object detectors were trained and benchmarked, including the YOLOv5/7/11/12 families and the transformer-based RT-DETR architecture. While the RT-DETR model achieved the highest precision overall, its computational complexity and heavy resource requirements limited its suitability for real-time deployment on low-cost embedded platforms. Conversely, the YOLOv11n model demonstrated the best accuracy–efficiency trade-off, reaching [email protected] = 0.872 at 320\$\times\$320 with 0.045 s/frame on a Jetson Nano, while lighter variants remained viable on Raspberry Pi boards. Across classes, gravel was the most reliably detected, and operating points around a confidence threshold of \$\tau \approx 0.31\$ yielded balanced F1 scores up to 0.82. Although results show that automatic road-condition monitoring on affordable hardware is feasible, the prototype has not yet undergone on-road field trials. It does not include an integrated rider alert module or energy-use assessment. These gaps define the immediate roadmap for deployment.},
  keywords = {road monitoring, computer vision, object detection, edge devices, advanced rider assistance systems},
  issn = {3068-5079},
  publisher = {Institute of Central Computation and Knowledge}
}

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