Our team faced the challenge of developing the complete brain for an autonomous rover, a system that required visual perception and real-time decision-making on low-power hardware. This case study is a technical deep dive into our process: from the implementation of the Mask R-CNN segmentation model for accurate object identification, to the acceleration of performance on a Raspberry Pi using a Coral TPU accelerator. Discover how we overcame the challenges of model optimization and quantization in TensorFlow to deliver a robust and efficient Edge AI solution for intelligent automation.
From pixels to action: how we developed the brain of an autonomous rover with embedded computer vision
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