Fusion of low-level feature for FOD classification

Han, Zhenqi; Fang, Yuchun; Xu, Hongmei

doi:10.1109/chinacom.2015.7497985

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2018

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Han et al [5,6] worked on FOD object recognition using a support vector machine (SVM) and random forest. FOD object recognition is to identify what the FOD is.…”

Section: Introductionmentioning

confidence: 99%

Foreign object debris material recognition based on convolutional neural networks

Han

Feng

et al. 2018

J Image Video Proc.

View full text Add to dashboard Cite

The material attributes of foreign object debris (FOD) are the most crucial factors to understand the level of damage sustained by an aircraft. However, the prevalent FOD detection systems lack an effective method for automatic material recognition. This paper proposes a novel FOD material recognition approach based on both transfer learning and a mainstream deep convolutional neural network (D-CNN) model. To this end, we create an FOD image dataset consisting of images from the runways of Shanghai Hongqiao International Airport and the campus of our research institute. We optimize the architecture of the D-CNN by considering the characteristics of the material distribution of the FOD. The results show that the proposed approach can improve the accuracy of material recognition by 39.6% over the state-of-the-art method. The work here will help enhance the intelligence capability of future FOD detection systems and encourage other practical applications of material recognition technology.

show abstract

“…Han et al [5,6] worked on FOD object recognition using a support vector machine (SVM) and random forest. FOD object recognition is to identify what the FOD is.…”

Section: Introductionmentioning

confidence: 99%

Foreign object debris material recognition based on convolutional neural networks

Han

Feng

et al. 2018

J Image Video Proc.

View full text Add to dashboard Cite

show abstract

Fusion of low-level feature for FOD classification

Cited by 1 publication

References 10 publications

Foreign object debris material recognition based on convolutional neural networks

Foreign object debris material recognition based on convolutional neural networks

Contact Info

Product

Resources

About