摘要
针对传统的图像处理方法对于机械零件检测存在的检测时间长、准确率低等难点,提出一种基于EfficientDet的汽车ECU分类检测方法,将经过预处理和数据增强的ECU外壳图片样本输入神经网络训练,利用一种改进的新型的加权双向特征提取网络BiFPN和一种复合尺度扩张方法进行特征提取并匹配特征图,提高检测的准确率,利用预训练模型进行迁移学习缩减训练时长,实现ECU外壳的自动检测。将检测结果与Faster R-CNN、Mask R-CNN、EfficientDet-D0模型检测结果相比较,实验结果表明,基于EfficientDet的机械零件检测算法的识别率高于对比的其他网络模型,mAP达92.4%,在实际应用中更能够精确地检测ECU零件,满足实验与生产线检测需求。
In order to solve the problem of the traditional image processing method,it is difficult to detect machine parts with long detection time and low precision,and EfficientDet-based ECU classification method was proposed.The pre-processed and data enhanced ECU shell samples were fed into the neural network training.An improved weighted bidirectional feature extraction network BiFPN and a compound scale expansion method were used to extract features and match the feature map to improve the precision of detection.The pre-training model was used for transfer learning to reduce the training time,and the automatic detection of ECU shell was realized.The results were compared with the Faster R-CNN,Mask R-CNN and EfficientDet-D0 models.The EfficientDet-based algorithm achieved a better recognition rate than its peer networks,with mAP reaching 92.4%.In practical applications,it can more accurately detect ECU parts to meet the requirements of test and production line testing.
作者
陈文韵
王学影
胡晓峰
郭斌
CHEN Wenyun;WANG Xueying;HU Xiaofeng;GUO Bin(College of Metrology and Measurement Engineering,China Jiliang University,Hangzhou 310018,China;Hangzhou Wolei Intelligent Technology Co.,Ltd.,Hangzhou 310018,China)
出处
《中国测试》
CAS
北大核心
2023年第1期98-104,共7页
China Measurement & Test
基金
国家自然科学基金(52075511)。
