Deep learning based Machinery Threat Detection on Pipeline Right of Way

Authors

Presenter(s)

Ruixu Liu

Files

Description

In this research, we develop a new deep learning strategy for robust detection and classification of objects on the pipeline right of way from aerial images. Our method can detect machinery threat with multiple sizes, different orientation and complex background in aerial images. In the proposed framework, the skip connection is used in the CNN structure to enhance feature learning. Also, the focal loss function is used to substitute for conventional cross entropy loss function in both of the region proposed network layer and the classifier layer.

Publication Date

4-24-2019

Project Designation

Graduate Research

Primary Advisor

Vijayan K. Asari

Primary Advisor's Department

Electrical and Computer Engineering

Keywords

Stander Symposium project

Recommended Citation

"Deep learning based Machinery Threat Detection on Pipeline Right of Way" (2019). Stander Symposium Projects. 1701.
https://ecommons.udayton.edu/stander_posters/1701