# LM-DeeplabV3+: A Lightweight Image Segmentation Algorithm Based on Multi-Scale Feature Interaction
## 4 Conclusions  
This paper proposes an improved LM-DeeplabV3+ model to solve the problems of large parameter counts, high computational resource requirements, insufficient attention to detail information, and the insufficient accuracy of lightweight models in existing semantic segmentation models. Firstly, the backbone network is replaced with the lightweight network MobilenetV2, and the ECA attention mechanism is introduced after the shallow features of MobilenetV2 to improve segmentation performance without damaging the network structure. The method of replacing the ASPP pool of the original ASPP module with a strip pool can be applied to more complex image scenes. Furthermore, the EPSA attention module is introduced to effectively establish long-term dependencies between multi-scale channel attention and achieve the cross-dimensional channel attention interaction of important features. Then, we designed a loss function suitable for multi-class tasks. Although CE loss performs well in segmentation tasks, there may be a problem of training results shifting towards a larger number of categories when faced with imbalanced categories, and the measurement of CE loss on the validation set usually cannot indicate the quality of segmentation well. Therefore, we designed a loss function that combines CE loss and Lovasz loss. After using CE to find the direction of gradient descent, the Lovasz loss is used to directly optimize the semantic segmentation metric mIoU. Compared to using only CE loss or Lovasz loss, this can achieve better segmentation results and we named it CL loss. This paper was validated on the Cityspaces dataset and found that:  
(1) LM-DeeplabV3+ effectively improves accuracy while significantly reducing parameter and computational complexity. The mIoU reaches 74.9%, which is 3.56% higher than DeeplabV3+. The mPA reaches 83.01%, which is 2.53% higher than the basic network. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Backbone** & **Attention** & **mIoU/\%** & **mPA/\%** \\ \hline MobilenetV2 & & 72.25 & 81.00 \\ MobilenetV2 & ECA & 72.80 & 81.30 \\ MobilenetV2 & EPSA & 73.56 & 82.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of different attention mechanisms on DeeplabV3+. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Backbone** & **Loss Function** & **mIoU/\%** & **mPA/\%** \\ \hline MobilenetV2 & CE loss & 72.55 & 81.00 \\ MobilenetV2 & Focal loss & 73.55 & 81.64 \\ MobilenetV2 & Lovasz loss & 74.77 & 82.71 \\ MobilenetV2 & CL loss & 74.90 & 83.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of different loss functions on LM-DeeplabV3+. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** &
\begin{tabular}{c} **Strip** \\ **Pooling** \\ \end{tabular} & **EPSA** & **ECA** & **CL Loss 2** & **mIoU/\%** & **mPA/\%** \\ \hline
1 & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 72.55 & 81.00 \\
2 & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & 73.49 & 81.67 \\
3 & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 73.60 & 82.02 \\
4 & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & 73.83 & 81.95 \\
5 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 74.90 & 83.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Different combinations of ablation experiments. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Backbone** & **Pooling** & **mIoU/\%** & **mPA/\%** \\ \hline MobilenetV2 & ASPP Pooling & 72.55 & 81.00 \\ MobilenetV2 & Strip Pooling & 73.28 & 81.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of different pooling layers on DeeplabV3+. (2) LM-DeeplabV3+ significantly improves the IoU of elements such as pole, traffic light, traffic sign, terrain, person, rider, bicycle, etc. Among these, the improvement in traffic light and pole was the largest, reaching 17.73% and 13.74%, respectively. Overall, it has a significant positive impact on segmentation performance.