Detection Method and Application of Apparent Diseases on Building External Walls Using Visual Recognition

JIN Yongqiang; ZHAO Zeming; YANG Yuan; GAO Changling; ZHENG Xiaowei

doi:10.3724/j.gyjzG26030304

Volume 56 Issue 5

May 2026

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JIN Yongqiang, ZHAO Zeming, YANG Yuan, GAO Changling, ZHENG Xiaowei. Detection Method and Application of Apparent Diseases on Building External Walls Using Visual Recognition[J]. INDUSTRIAL CONSTRUCTION, 2026, 56(5): 29-36. doi: 10.3724/j.gyjzG26030304

Citation:

JIN Yongqiang, ZHAO Zeming, YANG Yuan, GAO Changling, ZHENG Xiaowei. Detection Method and Application of Apparent Diseases on Building External Walls Using Visual Recognition[J]. INDUSTRIAL CONSTRUCTION, 2026, 56(5): 29-36. doi: 10.3724/j.gyjzG26030304

Citation:

JIN Yongqiang, ZHAO Zeming, YANG Yuan, GAO Changling, ZHENG Xiaowei. Detection Method and Application of Apparent Diseases on Building External Walls Using Visual Recognition[J]. INDUSTRIAL CONSTRUCTION, 2026, 56(5): 29-36. doi: 10.3724/j.gyjzG26030304

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Detection Method and Application of Apparent Diseases on Building External Walls Using Visual Recognition

doi: 10.3724/j.gyjzG26030304

1. Technology Center of China MCC5 Group Corp., Ltd., Chengdu 610063, China;
2. School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China

Received Date: 2026-03-03
Available Online: 2026-06-06
Publish Date: 2026-05-20

Abstract

Abstract

Aiming at the problems of low efficiency， strong reliance on manual labor， high risk of high-altitude work， and secondary damage that is easily caused by contact detection in traditional methods for building exterior wall disease detection， this paper proposes an intelligent non-destructive detection method based on machine vision and deep learning. This method enables rapid identification of three types of apparent diseases： spalling， hollowing， and cracking. Using UAV high-precision collection equipment， disease images were collected from typical exterior wall types such as tiles， paint， and cement mortar. A building exterior wall disease image database containing 1018 images of three types of diseases was constructed. Through LabelMe software， disease annotation was performed， forming 1168 spalling labels， 1619 hollowing labels， and 1515 cracking labels. Based on the deep learning YOLO11n model， multiple training schemes were implemented on the training set. This study found that， with 300 training epochs， an image size of 1280 pixels， and data augmentation enabled， a detection performance of mAP50 = 0.753 was achieved. This model relatively accurately identified the three types of apparent diseases： spalling， hollowing， and cracking. Finally， engineering instance applications were carried out in multiple residential communities in the Chengdu area， further proving that the model has good generalization ability and can provide a new technology for non-destructive rapid detection of building exterior wall diseases.
- building exterior walls,
- apparent diseases,
- deep learning,
- non-destructive detection,
- YOLO model

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References(27)

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