基于改进 EfficientNet 的锻件磁粉探伤智能检测方法研究

针对锻件生产企业零件缺陷检测效率低下,检测精度不高的问题,提出一种基于改进EfficientNet模型(EfficientNet-F),对两种锻件的荧光磁粉探伤图像进行检测。构建以EfficientNet为主干特征提取网络的深度学习模型,并引入特征金字塔为特征融合层,进而提高模型的多尺度特征融合能力;引入完备交并比和注意力机制以提高模型鲁棒性和检测效率。同时,搭建荧光磁粉探伤图像采集平台,构建缺陷样本数据集。试验表明,EfficientNet-F的最优模型在测试集上的均值平均精度达到了95.03%。F1得分值为0.96,浮点运算数为1.86 B。相较于其他深度学习模型,该方法提高了检测的精度和效率,可以满足相关生产企业的需求。

An intelligent magnetic particle testing method for forgings based on the improved EfficientNet

Aiming at the problems of low efficiency and low detection accuracy of parts defects in forging manufacturers, an improved EfficientNet model (EfficientNet-F) is proposed to detect the fluorescent magnetic particle flaw detection images of two kinds of forgings. A deep learning model with EfficientNet as the backbone feature extraction network is formulated, and the feature pyramid network is introduced as the feature fusion layer to improve the multi-scale feature fusion ability of the model. Complete intersection over union and attention mechanism are utilized to improve the robustness and detection efficiency of the model. Meanwhile, the fluorescent magnetic particle flaw detection image acquisition platform and the defective sample data set are both established. Experimental results show that the mean average precision precision of the optimal model of EfficientNet-F on the test set reaches 95. 03% . The F1 score is 0. 96 and the floating point operations is 1. 86 B. Compared with other deep learning models, the proposed method improvec the detection accuracy and efficiency. It can meet the needs of relevant production enterprises.