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Advanced Configuration Lab  · On-demand
Drone Landing Identification an Intel AI Reference Kit Lab
Advanced Configuration Lab
Details
Goals & objectives
Hardware & software
Solution overview
This lab will walk you through one of Intel's AI Reference Kits to develop an optimized semantic segmentation solution based on the Visual Geometry Group (VGG)-UNET architecture, aimed at assisting drones in safely landing by identifying and segmenting paved areas. The proposed system utilizes Intel® oneDNN optimized TensorFlow to accelerate the training and inference performance of drones equipped with Intel hardware. Additionally, Intel® Neural Compressor is applied to compress the trained segmentation model to further enhance inference speed. Explore the Developer Catalog for information on various use cases.
What you will see:
- Training and inferring with complex neural networks like the VGG-UNET model can be computationally demanding. In this reference kit, TensorFlow is optimized using Intel oneDNN to enhance performance on Intel hardware during both training and inference stages.
- Intel® Neural Compressor library further boosts inference efficiency. This project utilizes it to convert the trained FP32 VGG-UNET model into an INT8 CRNN model through post-training quantization, reducing model size and enhancing inference speed.