TY  - CONF
AU  - Xavier Soria
AU  - Yachuan Li
AU  - Mohammad Rouhani
AU  - Angel Sappa
A2  - ICCVW
PY  - 2023//
TI  - Tiny and Efficient Model for the Edge Detection Generalization
BT  - Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops
N2  - 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.
UR  - https://openaccess.thecvf.com/content/ICCV2023W/RCV/html/Soria_Tiny_and_Efficient_Model_for_the_Edge_Detection_Generalization_ICCVW_2023_paper.html
L1  - http://158.109.8.37/files/SLR2023.pdf
N1  - MSIAU
ID  - Xavier Soria2023
ER  -