A Method for Crack Pattern Recognition of Brick Walls Based on the SSG-YOLOv8n Model

CHEN Kui; ZHAO Yawei; WANG Guangming; LIANG Jianguo

doi:10.3724/j.gyjzG25051205

Volume 55 Issue 7

Jul. 2025

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Article Navigation > INDUSTRIAL CONSTRUCTION > 2025 > 55(7): 152-161

CHEN Kui, ZHAO Yawei, WANG Guangming, LIANG Jianguo. A Method for Crack Pattern Recognition of Brick Walls Based on the SSG-YOLOv8n Model[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(7): 152-161. doi: 10.3724/j.gyjzG25051205

Citation:

CHEN Kui, ZHAO Yawei, WANG Guangming, LIANG Jianguo. A Method for Crack Pattern Recognition of Brick Walls Based on the SSG-YOLOv8n Model[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(7): 152-161. doi: 10.3724/j.gyjzG25051205

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A Method for Crack Pattern Recognition of Brick Walls Based on the SSG-YOLOv8n Model

doi: 10.3724/j.gyjzG25051205

1. School of Civil and Environmental Engineering, Changsha University of Science and Technology, Changsha 410000, China;
2. Hunan Provincial Key Laboratory of Green Construction and Maintenance of Bridges and Buildings, Changsha University of Science and Technology, Changsha 410000, China;
3. Sichuan Hongtu Urban Design & Consulting Group Limited Inner Mongolia Design & Consulting Branch Company, Ordos 017200, China

Received Date: 2025-05-12
Available Online: 2025-09-12

Abstract

Abstract

The formation and development of cracks in brick walls is a gradual process. If the management unit ignores dynamic changes during daily inspections， it is highly likely to pose a threat to the safety of people's lives and property. To address the problems of high risk， low efficiency， and high cost existing in traditional manual detection methods， this paper proposes an improved SSG-YOLOv8n model. Based on the basic architecture of YOLOv8n， this model significantly enhances the recognition and detection accuracy of the crack patterns of brick walls by introducing the SPD Conv convolution module， adding a small target detection head， and embedding the SAFM attention mechanism module. The experimental data showed that the mAP@50 index of the improved model was 4.3% higher than that of the original YOLOv8n model. To balance detection accuracy improvement and model complexity control， the GhostNet lightweight module was further integrated. While maintaining the accuracy advantage， it significantly reduced floating-point operations， parameter count， and model size. The experimental results indicated that the improved model achieved both high detection accuracy and computational efficiency， providing important technical support for automated crack pattern detection in brick walls.
- cracks in brick walls,
- crack pattern recognition,
- YOLOv8,
- deep learning,
- intelligent detection

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References(19)

References

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