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This is a Experimental Study study (also classified as: Quantitative Study).

A road damage detection model based on improved YOLOv11s

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This research demonstrates improved detection accuracy in road damage using the YOLOv11s model, highlighting methodology enhancements and outcomes.

The improved model shows a 5.2% increase in mAP@0.5 and a 4.7% rise in F1-score, validating its effectiveness over previous methods.
Key enhancements include the replacement of the SPPF module and the introduction of the M-Head-T4 structure for better accuracy.
Data was collected using vehicle-mounted cameras to automate the detection process across various datasets.
The study aims to shift from semi-automated approaches to a fully automated road damage detection model.

Ma et al. (2025) studied this question.

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View Full Paper

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Authors

This research demonstrates improved detection accuracy in road damage using the YOLOv11s model, highlighting methodology enhancements and outcomes.

The improved model shows a 5.2% increase in mAP@0.5 and a 4.7% rise in F1-score, validating its effectiveness over previous methods.
Key enhancements include the replacement of the SPPF module and the introduction of the M-Head-T4 structure for better accuracy.
Data was collected using vehicle-mounted cameras to automate the detection process across various datasets.
The study aims to shift from semi-automated approaches to a fully automated road damage detection model.

Ma et al. (2025) studied this question.

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