TY - GEN
T1 - Identification and Assessment of Coating Diseases on Railway Steel Bridges Based on UAV Images
AU - Liu, Pengshuai
AU - Qin, Yong
AU - Meng, Fanteng
AU - Cui, Jing
AU - Xu, Liqian
AU - Qiu, Ninghai
AU - Wang, Zhipeng
AU - Yu, Chongchong
N1 - Publisher Copyright:
© Beijing Paike Culture Commu. Co., Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - The prolonged service time and natural environment of railway steel bridges lead to blistering, peeling and rust of the coating surface, which pose a potential threat to the safety of railway operations and require accurate and timely disease detection and assessment. UAVs, with their flexible aerial perspective and high operational safety, provide a powerful alternative for coating inspection. However, the current deep learning-based image recognition methods are often faced with challenges such as small target scale, multiple discrete, irregular, non-significant features of some disease edges, and complex background interference in non-coated areas when applied to this scenario. For this reason, this paper constructs a system for detecting and evaluating coating diseases on railway steel bridges. Firstly, the system effectively suppresses the background noise interference by extracting the foreground region of the coating and image slicing preprocessing technology. Subsequently, a multi-stage target aggregation algorithm based on image area-level reasoning is developed, which achieves accurate aggregation of multiple discrete similar targets in close neighbourhoods and the same disease target in adjacent sub-images; and further quantitatively evaluates the deterioration level of the disease by using the percentage of disease area.
AB - The prolonged service time and natural environment of railway steel bridges lead to blistering, peeling and rust of the coating surface, which pose a potential threat to the safety of railway operations and require accurate and timely disease detection and assessment. UAVs, with their flexible aerial perspective and high operational safety, provide a powerful alternative for coating inspection. However, the current deep learning-based image recognition methods are often faced with challenges such as small target scale, multiple discrete, irregular, non-significant features of some disease edges, and complex background interference in non-coated areas when applied to this scenario. For this reason, this paper constructs a system for detecting and evaluating coating diseases on railway steel bridges. Firstly, the system effectively suppresses the background noise interference by extracting the foreground region of the coating and image slicing preprocessing technology. Subsequently, a multi-stage target aggregation algorithm based on image area-level reasoning is developed, which achieves accurate aggregation of multiple discrete similar targets in close neighbourhoods and the same disease target in adjacent sub-images; and further quantitatively evaluates the deterioration level of the disease by using the percentage of disease area.
KW - Coating disease segmentation
KW - UAV imagery
KW - deep learning
KW - railway steel bridges
KW - region-level inference
UR - https://www.scopus.com/pages/publications/105050429864
U2 - 10.1007/978-981-92-2258-2_20
DO - 10.1007/978-981-92-2258-2_20
M3 - 会议稿件
AN - SCOPUS:105050429864
SN - 9789819222575
T3 - Lecture Notes in Electrical Engineering
SP - 208
EP - 215
BT - Proceedings of the 7th International Conference on Electrical Engineering and Information Technologies for Rail Transportation, EITRT 2025 - Volume II
A2 - Nie, Lei
A2 - Meng, Xuelei
A2 - Li, Shuaibing
A2 - Sun, Jixing
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th International Conference on Electrical Engineering and Information Technologies for Rail Transportation, EITRT 2025
Y2 - 17 October 2025 through 19 October 2025
ER -