作者:陈文韵1, 王学影1, 胡晓峰1, 郭斌2
作者单位:1. 中国计量大学计量测试工程学院,浙江 杭州 310018;
2. 杭州沃镭智能科技股份有限公司,浙江 杭州 310018
关键词:深度学习;EfficientDet;卷积神经网络;目标检测
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。
EfficientDet based automotive ECU classification and testing method
CHEN Wenyun1, WANG Xueying1, HU Xiaofeng1, GUO Bin2
1. College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China;
2. Hangzhou Wolei Intelligent Technology Co., Ltd., Hangzhou 310018, China
Abstract: 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.
Keywords: deep learning;EfficientDet;convolutional neural network;target detection
2023, 49(1):98-104 收稿日期: 2021-07-07;收到修改稿日期: 2021-09-27
基金项目: 国家自然科学基金(52075511)
作者简介: 陈文韵(1997-),女,浙江温州市人,硕士研究生,专业方向为机器视觉、深度学习
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