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.