Tiny and Efficient Model for the Edge Detection Generalization
Xavier Soria Yachuan Li Mohammad Rouhani Angel D. Sappa
Most high-level computer vision tasks rely on low-level image operations as
their initial processes. Operations such as edge detection, image enhancement,
and super-resolution, provide the foundations for higher level image analysis.
In this work we address the edge detection considering three main objectives:
simplicity, efficiency, and generalization since current state-of-the-art
(SOTA) edge detection models are increased in complexity for better accuracy.
To achieve this, we present Tiny and Efficient Edge Detector (TEED), a light
convolutional neural network with only $58K$ parameters, less than $0.2$% of
the state-of-the-art models. Training on the BIPED dataset takes $less than 30
minutes$, with each epoch requiring $less than 5 minutes$. Our proposed model
is easy to train and it quickly converges within very first few epochs, while
the predicted edge-maps are crisp and of high quality. Additionally, we propose
a new dataset to test the generalization of edge detection, which comprises
samples from popular images used in edge detection and image segmentation. The
source code is available in https://github.com/xavysp/TEED.