ORIGINAL RESEARCH article
Front. Comput. Sci.
Sec. Computer Vision
EAC-YOLO: A Surface damage identification method of lightweight membrane structure based on improved YOLO11
Provisionally accepted
- Beijing Technology and Business University, Beijing, China
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Different surface damage can cause harm to membrane structures, and traditional manual inspection methods are inefficient and prone to missed detections and false alarms. At the same time, the current mainstream detection algorithms are highly complex, which is not conducive to deployment on resource-constrained devices. To achieve automatic identification of typical surface damage in membrane structures, we construct a dataset comprising five damage types based on common types of surface damage in membrane structures and propose a lightweight identification algorithm for membrane structure surface damage, specifically EAC-YOLO. Firstly, the SPPF module is reconstructed, and the ECA lightweight attention mechanism is introduced to enhance the model's ability to distinguish easily confused features. Secondly, ADown is introduced to replace the original down-sampling method, improving the retention ability of multi-scale damage features. Finally, the CGBlock and C3k2 modules are combined and reconstructed in the neck network to reduce the interference of damage background factors and capture more features of the damage and its surrounding environment. Experimental evaluation results on the established dataset show that the improved mAP50 value reaches 87.5%, and the number of parameters, computational cost, and model size are reduced by approximately 28%, 25%, and 28%, respectively, compared with the original model, demonstrating the advantages of a small size and high accuracy.
Keywords: membrane structure1, damage identification2, YOLO113, target detection4, deeplearning5
Received: 06 Sep 2025; Accepted: 16 Dec 2025.
Copyright: © 2025 Yin, Limei, Liu, Du and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhang Limei
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