Encoder-Only Semantic Segmentation for Embedded Systems

The patent extracts and removes the computationally expensive decoder module from the traditional encoder-decoder architecture, retaining only the encoder to generate initial predictions. This extraction eliminates the majority of computational operations while preserving the essential feature extraction capabilities, thereby achieving real-time processing speeds without completely sacrificing accuracy through subsequent refinement steps.

The patent segments the semantic segmentation task into distinct stages: initial prediction by the encoder, boundary detection through edge algorithms, and refinement through pixel-wise classification. This segmentation allows each component to operate efficiently at its optimal computational level, with the encoder handling high-level feature extraction and simpler algorithms handling local details.

The patent removes the decoder module entirely from the system, eliminating millions of trainable parameters associated with it. The solution replaces the decoder's upsampling and refinement functions with alternative approaches that require minimal parameters, such as edge detection algorithms and simple pixel-wise classification based on encoder outputs.

The patent replaces the expensive, complex decoder with computationally inexpensive operations that can be executed rapidly. Instead of using a large neural network with millions of parameters for refinement, the system employs simple algorithms with negligible parameter counts, achieving similar functional outcomes with fraction of the computational resources.

The patent substitutes the reliance on expensive specialized hardware (GPUs) with algorithmic optimizations that enable efficient execution on standard processors. By replacing complex neural network operations with simpler mathematical operations and leveraging CPU cache efficiency, the system achieves comparable processing speeds without requiring costly GPU hardware.

The patent changes the computational parameters of the system by eliminating operations that require high computational power and focusing on algorithms with lower computational complexity. This parameter change in the algorithmic approach allows the system to run efficiently on commodity hardware, dramatically reducing the hardware cost requirement while maintaining acceptable performance levels.