  Recently, Deep-Neural-Network (DNN) based edge prediction is progressing
fast. Although the DNN based schemes outperform the traditional edge detectors,
they have much higher computational complexity. It could be that the DNN based
edge detectors often adopt the neural net structures designed for high-level
computer vision tasks, such as image segmentation and object recognition. Edge
detection is a rather local and simple job, the over-complicated architecture
and massive parameters may be unnecessary. Therefore, we propose a traditional
method inspired framework to produce good edges with minimal complexity. We
simplify the network architecture to include Feature Extractor, Enrichment, and
Summarizer, which roughly correspond to gradient, low pass filter, and pixel
connection in the traditional edge detection schemes. The proposed structure
can effectively reduce the complexity and retain the edge prediction quality.
Our TIN2 (Traditional Inspired Network) model has an accuracy higher than the
recent BDCN2 (Bi-Directional Cascade Network) but with a smaller model.
