Intelligent road crack detection and analysis based on improved YOLOv8

2025-04-16

AI Summary

To address low efficiency and poor accuracy in urban road crack detection, this paper proposes an enhanced YOLOv8 model integrating dual attention mechanisms—ECA and CBAM—within a unified end-to-end framework for crack detection, segmentation, and geometric quantification. Innovatively, it introduces the first collaborative dual-attention mechanism into YOLOv8 to strengthen feature representation of small-scale cracks. A lightweight semantic segmentation module and a custom geometric parameter extraction algorithm are incorporated to achieve high-precision crack localization, pixel-level segmentation, and automatic computation of critical metrics—including maximum/minimum width and spatial coordinates. Trained on 4,029 annotated images, the model achieves an mAP@0.5 of 92.7%, a segmentation IoU of 98.1% on real-world scenes (a 11.4% improvement over the baseline), and a width measurement error of less than 0.3 mm.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Algorithm Configuration

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

As urbanization speeds up and traffic flow increases, the issue of pavement distress is becoming increasingly pronounced, posing a severe threat to road safety and service life. Traditional methods of pothole detection rely on manual inspection, which is not only inefficient but also costly. This paper proposes an intelligent road crack detection and analysis system, based on the enhanced YOLOv8 deep learning framework. A target segmentation model has been developed through the training of 4029 images, capable of efficiently and accurately recognizing and segmenting crack regions in roads. The model also analyzes the segmented regions to precisely calculate the maximum and minimum widths of cracks and their exact locations. Experimental results indicate that the incorporation of ECA and CBAM attention mechanisms substantially enhances the model's detection accuracy and efficiency, offering a novel solution for road maintenance and safety monitoring.

Problem

• Detects road cracks using improved YOLOv8 for safety
• Replaces manual inspection with efficient deep learning model
• Measures crack widths and locations for maintenance

Innovation

• Enhanced YOLOv8 for crack detection
• ECA and CBAM boost accuracy
• Segmentation model analyzes crack dimensions