Cargando…
Dense-RefineDet for Traffic Sign Detection and Classification
Detecting and classifying real-life small traffic signs from large input images is difficult due to their occupying fewer pixels relative to larger targets. To address this challenge, we proposed a deep-learning-based model (Dense-RefineDet) that applies a single-shot, object-detection framework (Re...
Autores principales: | Sun, Chang, Ai, Yibo, Wang, Sheng, Zhang, Weidong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698555/ https://www.ncbi.nlm.nih.gov/pubmed/33213025 http://dx.doi.org/10.3390/s20226570 |
Ejemplares similares
-
A Robust Fabric Defect Detection Method Based on Improved RefineDet
por: Xie, Huosheng, et al.
Publicado: (2020) -
CXR-RefineDet: Single-Shot Refinement Neural Network for Chest X-Ray Radiograph Based on Multiple Lesions Detection
por: Lin, Cong, et al.
Publicado: (2022) -
TransEffiDet: Aircraft Detection and Classification in Aerial Images Based on EfficientDet and Transformer
por: Wang, Yanfeng, et al.
Publicado: (2022) -
TD-Det: A Tiny Size Dense Aphid Detection Network under In-Field Environment
por: Teng, Yue, et al.
Publicado: (2022) -
CXray-EffDet: Chest Disease Detection and Classification from X-ray Images Using the EfficientDet Model
por: Nawaz, Marriam, et al.
Publicado: (2023)