激光与光电子学进展, 2020, 57 (4): 041515, 网络出版: 2020-02-20   

基于改进Faster RCNN的马克杯缺陷检测方法 下载: 1389次

Mug Defect Detection Method Based on Improved Faster RCNN
作者单位
哈尔滨理工大学自动化学院, 黑龙江 哈尔滨 150080
摘要
Faster RCNN在小目标的检测方面准确度和鲁棒性性能不够理想。为此,提出一种改进Faster RCNN对马克杯缺陷进行检测,将Faster RCNN和特征金字塔网络(FPN)相结合,增加使用细节化的浅层特征,使其对小目标有更好的检测效果。分别使用改进前后的Faster RCNN在Caffe上进行仿真。结果表明,Faster RCNN检测马克杯缺陷的性能出色,但是遗漏了部分小目标;改进后的Faster RCNN对缺口和划痕缺陷的检测准确率最多提升2.485个百分点,并且在小目标识别方面也有更优异的表现。
Abstract
Faster RCNN has poorer performance in terms of accuracy and robustness for detecting small targets. For this reason, an improved Faster RCNN was proposed to detect the defects in mugs. The Faster RCNN and feature pyramid network (FPN) were combined to increase the use of detailed shallow features, so as to achieve better detection effect for small targets. Faster RCNNs before and after improvement were used to conduct simulation on Caffe. The results show that Faster RCNN performs well in defect detection for mugs, but it misses some small targets. The improved Faster RCNN increases the detection accuracy by 2.485 percent at most for gaps and scratches and performs better in small target recognition.

李东洁, 李若昊. 基于改进Faster RCNN的马克杯缺陷检测方法[J]. 激光与光电子学进展, 2020, 57(4): 041515. Dongjie Li, Ruohao Li. Mug Defect Detection Method Based on Improved Faster RCNN[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041515.

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