FINED: Fast Inference Network for Edge Detection

In this paper, we address the design of lightweight deep learning-based edge detection. The deep learning technology offers a significant improvement on the edge detection accuracy. However, typical neural network designs have very high model complexity, which prevents it from practical usage. In co...

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Bibliographic Details
Published in:arXiv.org 2020-12
Main Authors: Wibisono, Jan Kristanto, Hsueh-Ming Hang
Format: Article
Language:English
Subjects:
Accuracy
Complexity
Deep learning
Edge detection
Inference
Lightweight
Neural networks
Training
Online Access:Get full text
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Summary:In this paper, we address the design of lightweight deep learning-based edge detection. The deep learning technology offers a significant improvement on the edge detection accuracy. However, typical neural network designs have very high model complexity, which prevents it from practical usage. In contrast, we propose a Fast Inference Network for Edge Detection (FINED), which is a lightweight neural net dedicated to edge detection. By carefully choosing proper components for edge detection purpose, we can achieve the state-of-the-art accuracy in edge detection while significantly reducing its complexity. Another key contribution in increasing the inferencing speed is introducing the training helper concept. The extra subnetworks (training helper) are employed in training but not used in inferencing. It can further reduce the model complexity and yet maintain the same level of accuracy. Our experiments show that our systems outperform all the current edge detectors at about the same model (parameter) size.
ISSN:2331-8422