# A lightweight YOLO11n seg framework for real time surface crack detection with segmentation

## TL;DR

This paper introduces a lightweight deep learning model for fast and accurate detection of surface cracks in infrastructure using the YOLO11n-seg framework.

## Contribution

The novel contribution is a lightweight YOLO11n-seg model that achieves real-time performance with strong segmentation accuracy for crack detection.

## Key findings

- The model achieves a Precision of 78.8% and a Box mAP@50 of 76.2% on the Crack-Seg dataset.
- It processes images in 3.6ms per image on Tesla T4, enabling ultra-fast real-time inspection.
- The model has 2.8 million parameters and a Mask mAP@50 of 58.7%, offering lower computational cost than traditional methods.

## Abstract

The recognition of superficial cracks is essential to ensure the safety, durability, and longevity of civil infrastructure such as bridges, pavements, tunnels, and buildings. Traditional crack detection methods have been largely based on manual inspections and classical image processing techniques, including edge detection, thresholding, and morphological operations. With the rapid advancement of computer vision and deep learning, significant progress has been made in automating crack detection. To gain insight into previous research, we reviewed some studies from the past few years and identified YOLO11 as the most suitable model for crack detection tasks. In this study, we propose a deep learning-based framework for surface crack detection using the Crack-Seg dataset and the YOLO11n-seg architecture. Experimental results demonstrate that YOLO11n-seg achieves strong performance on the Crack-Seg dataset. The suggested model reaches a Precision of 78.8%, which is comparable to heavy baselines. Our results show that the suggested lightweight model, with just 2.8 million parameters, has a Box mAP@50 of 76.2% with a Mask mAP@50 of 58.7%. Most importantly, the model reaches an inference rate of 3.6ms for each image (on Tesla T4), allowing for ultra-fast processing in highly automated inspection systems. These findings establish a new benchmark for edge-deployable crack recognition, demonstrating the possibility that the YOLO11n-seg architecture may provide acceptable segmentation performance with lower computational cost than large, traditional methods.