基于改进的Faster R-CNN算法的机械零件图像识别
郭斐; 靳伍银; 王猛
2019
Source Publication机械设计
ISSN1001-2354
Volume36Issue:9Pages:113-116
Abstract在传统的Faster R-CNN网络结构中减少原有的卷积层数,同时加入Inception结构层,提出一种基于Faster RCNN的零件识别的改进算法。该算法在保证不增加网络参数和计算量的前提下,增加深度和网络结构复杂度,进一步有效地提取图像的特征。结果表明:通过自制机械零件图像数据集,将传统Faster R-CNN与改进后的Faster R-CNN算法均成功应用于机械零件图像识别。与传统Faster R-CNN相比,基于改进后的Faster R-CNN深度学习算法识别机械零件的识别精度和准确度均更高。
KeywordFaster R-CNN算法 机械零件图像识别 Inception结构
Indexed ByCSCD
Language中文
WOS Research AreaAutomation & Control Systems
WOS SubjectAUTOMATION CONTROL SYSTEMS
CSCD IDCSCD:6581855
Document Type期刊论文
Identifierhttp://ir.lut.edu.cn/handle/2XXMBERH/75290
Collection机电工程学院
Affiliation兰州理工大学机械电子工程学院, 兰州, 甘肃 730050, 中国
First Author AffilicationLanzhou University of Technology
First Signature AffilicationLanzhou University of Technology
Recommended Citation
GB/T 7714
郭斐,靳伍银,王猛. 基于改进的Faster R-CNN算法的机械零件图像识别[J]. 机械设计,2019,36(9):113-116.
APA 郭斐,靳伍银,&王猛.(2019).基于改进的Faster R-CNN算法的机械零件图像识别.机械设计,36(9),113-116.
MLA 郭斐,et al."基于改进的Faster R-CNN算法的机械零件图像识别".机械设计 36.9(2019):113-116.
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