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Concept
SegNet is a deep convolutional neural network architecture designed for semantic pixel-wise segmentation, which is particularly effective in scenarios where precise object boundary delineation is crucial. It employs an encoder-decoder structure with a unique approach to upsampling that uses pooling indices from the encoder to preserve spatial information, improving segmentation accuracy.
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