H.3.2.2. Computer vision
Elahe Yadolahi; Sheis Abolmaali
Abstract
Semantic segmentation is a critical task in computer vision, focused on extracting and analyzing detailed visual information. Traditional artificial neural networks (ANNs) have made significant strides in this area, but spiking neural networks (SNNs) are gaining attention for their energy efficiency ...
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Semantic segmentation is a critical task in computer vision, focused on extracting and analyzing detailed visual information. Traditional artificial neural networks (ANNs) have made significant strides in this area, but spiking neural networks (SNNs) are gaining attention for their energy efficiency and biologically inspired time-based processing. However, existing SNN-based methods for semantic segmentation face challenges in achieving high accuracy due to limitations such as quantization errors and suboptimal membrane potential distribution. This research introduces a novel spiking approach based on Spiking-DeepLab, incorporating a Regularized Membrane Potential Loss (RMP-Loss) to address these challenges. Built upon the DeepLabv3 architecture, the proposed model leverages RMP-Loss to enhance segmentation accuracy by optimizing the membrane potential distribution in SNNs. By optimizing the storage of membrane potentials, where values are stored only at the final time step, the model significantly reduces memory usage and processing time. This enhancement not only improves the computational efficiency but also boosts the accuracy of semantic segmentation, enabling more accurate temporal analysis of network behavior. The proposed model also demonstrates better robustness against noise, maintaining its accuracy under varying levels of Gaussian noise, which is common in real-world scenarios. The proposed approach demonstrates competitive performance on standard datasets, showcasing its potential for energy-efficient image processing applications.
