PT Journal
AU Xavier Soria
   Gonzalo Pomboza-Junez
   Angel Sappa
TI LDC: Lightweight Dense CNN for Edge Detection
SO IEEE Access
JI ACCESS
PY 2022
BP 68281
EP 68290
VL 10
DI 10.1109/ACCESS.2022.3186344
AB 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
ER