TY  - JOUR
AU  - Xavier Soria
AU  - Gonzalo Pomboza-Junez
AU  - Angel Sappa
PY  - 2022//
TI  - LDC: Lightweight Dense CNN for Edge Detection
T2  - ACCESS
JO  - IEEE Access
SP  - 68281
EP  - 68290
VL  - 10
PB  - IEEE
N2  - This paper presents a Lightweight Dense Convolutional (LDC) neural network for edge detection. The proposed model is an adaptation of two state-of-the-art approaches, but it requires less than 4% of parameters in comparison with these approaches. The proposed architecture generates thin edge maps and reaches the highest score (i.e., ODS) when compared with lightweight models (models with less than 1 million parameters), and reaches a similar performance when compare with heavy architectures (models with about 35 million parameters). Both quantitative and qualitative results and comparisons with state-of-the-art models, using different edge detection datasets, are provided. The proposed LDC does not use pre-trained weights and requires straightforward hyper-parameter settings. The source code is released at https://github.com/xavysp/LDC
UR  - http://dx.doi.org/10.1109/ACCESS.2022.3186344
N1  - MSIAU; MACO; 600.160; 600.167
ID  - Xavier Soria2022
ER  -