Introduce

This repository contains the source code for our paper:

U-Net Semantic Segmentation-Based Calorific Value Estimation of Straw Multifuels for Combined Heat and Power Generation Processes

Functions

• It introduces a self-attention mechanism in the skip connections to enhance the extraction of key information from deep features;
• It replaces traditional convolutions with depthwise separable convolutions to reduce the model’s computational complexity and improve inference speed;
• It substitutes the bottleneck layer with a Transformer encoder to leverage the global modeling capabilities of Transformers, allowing the model to better understand contextual information within the image.

Model	PA(%)↑	mIoU(%)↑	Speed(ms)↓	Size(MB)
Unet	95.56	86.8	5.11	118.76
SegNET	94.554	84.8	4.26	112.325
DDRNet	87.655	71.8	2.62	21.7247
SmaAt-UNet	93.713	83.0	2.66	15.3824
Our model-large	96.309	89	5.45	129.69
Our model-small	95.359	86.6	3.44	31.176

Model Architecture

Results

Env set

• cuda 11.8
• python 3.8.0
• conda create --name StrawSeg python=3.8.0
• Detail please reference requierment.txt

About author

• warren@伟
• Blog：CSDN