摘要
为解决传统深度学习网络模型在轮胎X光瑕疵图像检测上识别率低、准确性差的问题,基于特征金字塔网络FPN提出一种多级特征提取网络TWFPN并将其与Ef-ficient-Net目标识别网络融合,得到高度融合语义和细节信息的瑕疵特征向量并改进检测算法流程;通过融合背景信息的检测算法对模型识别结果进行重新判定,得到最终的瑕疵类别和位置信息。对6种轮胎缺陷类型X光图片进行测试表明,改进检测算法显著提高了模型的识别精度,具有良好的应用前景。
In order to solve the problem of low recognition rate and poor accuracy of traditional deep learning network models in tire X-ray defect image detection,this paper proposes a multi-level feature extraction network TWFPN based on the feature pyramid network FPN and integrates it with the Efficient-Net target recognition network to obtain the feature vector of the flaw with a high degree of fusion of semantic and detailed information and improve the detection algorithm process.Through the fusion of the background information,the model recognition result is re-judged,and the final flaw category and location information are obtained.Tests on X-ray images of six types of tire defects show that these improvements have significantly improved the recognition accuracy of the model,and have good application prospects.
作者
陈亮
白文涛
CHEN Liang;BAI Wentao(Shenyang Ligong University,Shenyang 110159,China)
出处
《沈阳理工大学学报》
CAS
2021年第2期8-14,共7页
Journal of Shenyang Ligong University
基金
辽宁省教育厅基本科研项目(LG201707)
辽宁省自然科学基金项目(20170540788)
国家重点研发计划项目(2017YFC0821001)。
关键词
深度学习
目标识别
特征提取
网络融合
检测算法
deep learning
target recognition
feature extraction
network fusion
detection algorithm
作者简介
陈亮(1979-),男,教授,博士,研究方向:人工智能、嵌入式等。
