TY  - RPRT
U1  - Arbeitspapier
A1  - Gangadharaiah, Dhanush
T1  - Synthetic Data Generation for Enhanced Computer Vision applications: A CAD model and Blender approach
N2  - In today's AI-driven era, computer vision, including autonomous driving, robotics, and healthcare, is prevalent. How-ever, acquiring ample data while managing resources and privacy constraints is challenging. This article proposes a solution: synthetic data generation. We use CAD software to craft intricate 3D models, process them in Blender, and evaluate quality using metrics like Structural Similarity and PSNR (Peak Signal to Noise Ratio). Synthetic data achieves up to 90% similarity with real data and an average PSNR of 21dB. Our method offers a streamlined, dependable ap-proach for enhancing computer vision, especially in object detection, addressing data acquisition challenges.
KW  - Image Processing
KW  - Realistic Data Generation
KW  - Structural Similarity Index
KW  - Peak Signal to Noise Ratio
Y1  - 2023
U6  - https://nbn-resolving.org/urn:nbn:de:bsz:747-opus4-7481
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:747-opus4-7481
SP  - 7
S1  - 7
ER  -