中图分类号：TP391.41 文献标志码：A DOI: 10.16157/j.issn.0258-7998.257256 中文引用格式： 马运发，葛承强，陈振斌，等. 基于改进YOLOv11n的碳钢表面腐蚀检测算法[J]. 电子技术应用，2026，52(8)：80-86. 英文引用格式： Ma Yunfa，Ge Chengqiang，Chen Zhenbin，et al. Carbon steel surface corrosion detection algorithm based on improved YOLOv11n[J]. Application of Electronic Technique，2026，52(8)：80-86.
Carbon steel surface corrosion detection algorithm based on improved YOLOv11n
Ma Yunfa，Ge Chengqiang，Chen Zhenbin，Lin Luorui，Li Jiajun
College of Mechanical and Electrical Engineering，Hainan University
Abstract： Aiming to address the problems of low recognition accuracy in current metal corrosion identification models and the difficulty of deploying parameter-heavy models on mobile and embedded devices with limited computational resources, this paper proposes a lightweight corrosion detection model based on YOLOv11n. The model adopts MobileNetV4 as the backbone network to effectively reduce model parameters and computational complexity. A Cross-scale Feature Fusion Module (CCFM) is used to optimize the neck network structure, which improves multi-scale target detection accuracy while reducing model parameters and computational load. The Bi-temporal Feature Aggregation Module (BFAM) is integrated into the C3K2 module to significantly enhance the model's perception capability for corrosion texture details. Finally, the Inner-MPDIoU loss function is introduced to improve the model's convergence and stability. Experimental results show that compared with the original model, the improved model reduces parameters by 51.2% and computational complexity by 25.4%, while precision (P), recall (R), and mAP50 increase by 1.1%, 4.0%, and 0.6% respectively, demonstrating the effectiveness of the proposed algorithm.
Key words : YOLOv11n；corrosion detection；MobileNetV4；CCFM；BFAM；Inner-MPDIoU
