基于多尺度特征感知的SOFC表面缺陷检测算法OA

The solid oxide fuel cell(SOFC)is a key component of SOFC power generation systems,and its quality directly affects the stable operation of the cell stack and the service life of the system.To address issues such as the random shapes and sizes of surface defects on individual SOFCs,which can lead to false detection and missed detection,a surface defect detection algorithm for SOFCs based on multi-scale feature perception is proposed.Firstly,a multi-scale feature perception(MSFP)module is introduced to capture the positional information of target defects,enhance feature extraction in defect regions,and suppress interference from complex backgrounds.Secondly,an adaptive gated residual attention(AGRA)module is designed to enable the model to simultaneously capture fine-grained local features and global structural information.Finally,the Shape-IoU loss function is employed to focus on the shape and scale of the bounding boxes for surface defects,thereby optimizing the bounding box regression process.Experimental results show that the proposed algorithm achieves mean average precision,precision,and recall rates of 87.5%,85.1%,and 80.2%,respectively,on the SOFC surface defect dataset.Compared with other mainstream object detection algorithms,the proposed algorithm effectively mitigates false detection and missed detection,demonstrates strong robustness.