Building Surface Defect and Damage Detection Method based on YOLOv8-act

Wenhao Li; Huanxin Zhou; Jiaqi Li

doi:10.6919/ICJE.202602_12(2).0010

Authors

Wenhao Li
Huanxin Zhou
Jiaqi Li

DOI:

https://doi.org/10.6919/ICJE.202602_12(2).0010

Keywords:

YOLOv8-act; Building Surface Defect Detection; Object Detection; Deep Learning; Computer Vision.

Abstract

Detecting surface defects and damage on buildings is crucial for ensuring structural safety and durability. Traditional manual inspection methods are inefficient and highly subjective, making them inadequate for large-scale, high-precision engineering demands. This paper proposes a building surface defect detection method based on YOLOv8-act. By replacing the original SILU activation function with the SELU activation function-which possesses self-normalizing properties-the model's ability to extract features from low-contrast, irregularly shaped defects is enhanced. Experimental results demonstrate that the YOLOv8-act model achieves an mAP50 of 0.641, outperforming other compared models. This approach maintains real-time processing and deployment convenience while improving detection accuracy, providing an effective solution for automated and precise detection of building surface defects.

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