  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.
