@InProceedings{XavierSoria2023,
author="Xavier Soria
and Yachuan Li
and Mohammad Rouhani
and Angel Sappa",
title="Tiny and Efficient Model for the Edge Detection Generalization",
booktitle="Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops",
year="2023",
abstract="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.",
optnote="MSIAU",
optnote="exported from refbase (http://158.109.8.37/show.php?record=3941), last updated on Thu, 25 Jan 2024 18:19:36 +0100",
opturl="https://openaccess.thecvf.com/content/ICCV2023W/RCV/html/Soria_Tiny_and_Efficient_Model_for_the_Edge_Detection_Generalization_ICCVW_2023_paper.html",
file=":http://158.109.8.37/files/SLR2023.pdf:PDF"
}