Paulo César Costa
Papers
1
Total Citations
2
H-Index
1
About
Paulo César Costa is a researcher at the forefront of applying deep learning to industrial automation, with a particular focus on computer vision for manufacturing quality control. His primary research areas span convolutional neural network architectures, RGB-D image analysis, and the integration of smart sensing technologies into Industry 4.0 pipelines. Costa’s most notable contribution is the development of a branched Convolutional Neural Network designed for RGB-D image classification of ceramic pieces, a novel architecture that effectively fuses depth and color data to enhance defect detection and sorting accuracy on assembly lines. This work, published in 2024 and already garnering 2 citations, addresses a critical bottleneck in automated manufacturing by enabling robots to perform complex visual inspection tasks with greater reliability. By bridging the gap between 3D camera data and practical industrial applications, Costa is helping to pave the way for more intelligent, adaptive production systems. His research holds significant promise for reducing waste and improving efficiency in sectors ranging from ceramics to broader smart manufacturing environments.
Research Focus
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Top Papers
- 1