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Detection Method and Application of Apparent Diseases on Building External Walls Using Visual Recognition
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Yongqiang JIN1, Zeming ZHAO1, Yuan YANG1, Changling GAO1, Xiaowei ZHENG2
Industrial Construction | 2026, 56(5) : 29 - 36
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Industrial Construction | 2026, 56(5): 29-36
Detection Method and Application of Apparent Diseases on Building External Walls Using Visual Recognition
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Yongqiang JIN1, Zeming ZHAO1, Yuan YANG1, Changling GAO1, Xiaowei ZHENG2
Affiliations
  • 1Technology Center of China MCC5 Group Corp., Ltd., Chengdu610063, China
  • 2School of Mechanics and Civil;Engineering, China University of Mining and Technology, Xuzhou221116, China
Published: 2026-05-20 doi: 10.3724/j.gyjzG26030304
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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
Yongqiang JIN, Zeming ZHAO, Yuan YANG, Changling GAO, Xiaowei ZHENG. 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
Year 2026 volume 56 Issue 5
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doi: 10.3724/j.gyjzG26030304
  • Receive Date:2026-03-03
  • Online Date:2026-06-25
  • Published:2026-05-20
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  • Received:2026-03-03
Affiliations
    1Technology Center of China MCC5 Group Corp., Ltd., Chengdu610063, China
    2School of Mechanics and Civil;Engineering, China University of Mining and Technology, Xuzhou221116, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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