# LM-DeeplabV3+: A Lightweight Image Segmentation Algorithm Based on Multi-Scale Feature Interaction
## 3 Results
### Results and Analysis
#### 3.3.2 Comparison of Different Models  
In order to better validate the segmentation performance of the proposed model in this article, we selected different models for comparative analysis. Table 3 shows the experimental results of different models on the Cityscapes dataset. When using the same backbone network, both the mIoU and mPA of the DeeplabV3+ model are improved compared to the DeeplabV3 model. We found that the LM-DeeplabV3+ model showed significant improvement compared to the original DeeplabV3+ model with Xception as the backbone network. The mIoU and mPA increased by 3.69% and 2.53%, respectively, while params was one-fifth of the original model and FLOPs were one-quarter of the original model. Compared to LM-DeeplabV3+ with Resnet101 as the backbone network, the mIoU and mPA of LM-DeeplabV3+ decreased by 1.79% and 0.34%, respectively, but params was one eighth of the former and FLOPs was one-seventh of the former. Compared to the Deeplabv3+ model with MobileNetV2 as the backbone network, although the params and FLOPs of LM-DeeplabV3+ slightly increased, its mIoU and mPA increased by 2.35% and 2.01%, respectively. On most tasks and datasets, Xception probably has a higher accuracy than MobileNetV2. In our experiment, the results show that that the effect of Xception is worse than MobileNetV2 on the Cityscapes dataset, the for which reasons can be summarized as:  
(1) Xception performs poorly on the Cityscapes dataset. (2) In order to improve the convergence speed, all networks use pre-training weights, but the effect of Xception's pre-training weights is relatively poor. (3) Xception training requires a longer training cycle, and the epochs in our paper do not allow Xception to obtain the best training effect. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Crop\_size & 768 \(\times\) 768 \\ Batch\_size & 8 \\ Val\_batch\_size & 4 \\ Output\_stride & 16 \\ Lr & 0.1 \\ Lr\_policy & poly \\ Step\_size & 10,000 \\ Iterations & 30,000 \\ Weight\_decay & 0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental Parameter Settings. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Backbone** & **mIoU/\%** & **mPA/\%** & **Params/M** & **FLOPs/G 1** \\ \hline Deeplabv3 & Xception & 65.20 & 76.10 & 36.93 & 302.85 \\ Deeplabv3 & Resnet50 & 74.58 & 82.37 & 39.64 & 368.87 \\ Deeplabv3 & Resnet101 & 75.61 & 84.02 & 58.64 & 544.06 \\ Deeplabv3 & Mobilenetv2 & 71.43 & 79.89 & 5.11 & 48.46 \\ Deeplabv3+ & Xception & 71.21 & 80.48 & 37.05 & 324.65 \\ Deeplabv3+ & Resnet50 & 75.92 & 83.35 & 39.76 & 389.88 \\ Deeplabv3+ & Resnet101 & 76.69 & 83.35 & 58.75 & 565.07 \\ Deeplabv3+ & Mobilenetv2 & 72.55 & 81.00 & 5.22 & 69.07 \\ LM-Deeplabv3+ & 74.90 & 83.01 & 7.20 & 83.56 \\ \hline \hline \end{tabular}  
* The size of the image is (3,768,768).  
\end{table} 
Table 3: Comparison of Cityscapes validation set performance on different models. Nevertheless, this paper mainly improves the lightweight model, and experiments show that this paper can effectively reduce the size, calculation, and parameters of the model, and improve the accuracy of the lightweight model. DeeplabV3+ with Xception as the backbone network is only used as a comparison, which has no impact on the experiment in our paper. Meanwhile, comparisons with other networks (Resnet50 and Resnet101) do illustrate that lightweight models are less accurate than more complex models. From Figure 8, we can see that LM-DeeplabV3+ has a significant improvement in segmentation performance, which is closer to the label compared to DeeplabV3+. Compared to DeeplabV3+, LM-DeeplabV3+ has more accurate pixel recognition and fewer incorrect pixel recognitions. From Table 4 and Figure 9, we can find that our proposed model improved the segmentation performance of most street-view elements, with significant improvements in IoU for elements such as pole, traffic light, traffic sign, terrain, person, rider, and bicycle, which has a significant positive impact on segmentation performance.