Daniel Carreira
Papers
1
Total Citations
2
H-Index
1
About
Daniel Carreira is a researcher at the forefront of applying deep learning to industrial computer vision, with a particular focus on RGB-D image classification for manufacturing automation. His most-cited work, "A branched Convolutional Neural Network for RGB-D image classification of ceramic pieces" (2024), addresses a critical challenge in the fourth industrial revolution: how to effectively fuse color and depth data from 3D cameras to improve object recognition on assembly lines. Carreira’s key contribution lies in designing a branched CNN architecture that separately processes RGB and depth streams before merging them, enabling more robust classification of ceramic pieces—a task essential for quality control in automated factories. Though early in his career, his work has already garnered citations from researchers exploring smart sensor integration and robotic manipulation. By bridging the gap between cutting-edge neural network design and practical industrial needs, Carreira is helping pave the way for more intelligent, adaptable manufacturing systems. His research sits at the intersection of computer vision, deep learning, and Industry 4.0, promising significant impact as automation continues to evolve.
Research Focus
Key Achievements
Top Papers
- 1