# 2D Semantic Segmentation: Recent Developments and Future Directions
## 5 Common Data Sets and Evaluation Indicators
However, DDRNet was mainly designed for road scene segmentation and may require adjustment and retraining for other scenes. Training and debugging may be difficult and require high-performance computing resources due to the use of a complex feature fusion strategy. MIFNet (proposed in Ref. [113]) is a lightweight neural network architecture that reduces network parameters and computational complexity while maintaining model accuracy and improving real-time performance on mobile devices. MIFNet adopts multiscale information fusion technology to extract information from feature maps of different scales. It enhances the model's receptive field and improves its recognition and understanding of objects. Experimental verification on multiple publicly available data sets demonstrated the good performance of MIFNet. However, the network structure of MIFNet is simple and may have limited processing capabilities for complex scenes and tasks. The method requires parameter and hyperparameter tuning to achieve optimal performance. Ref. [114] proposed a new neural network model called ViT-A, which adapts the ViT model to dense prediction tasks and improves the accuracy of image segmentation by using the adapter method. The method has high computational efficiency and flexibility and can be used for transfer learning on different data sets and tasks. Experimental results showed the method's good performance in various complex scenarios. However, the ViT-A method has limitations in the size and scale of input images and requires preprocessing and scaling before segmentation. The adapter structure of ViT-A also requires certain computational resources that may affect the method's real-time performance and deployability. Ref. [115] proposed a neural network model called DRAN, which uses a dual-relation-aware mechanism to model the relationships between different objects in images and improve the accuracy of scene segmentation. The method incorporates an attention mechanism that can adaptively focus on important regions in images, thus reducing the interference of redundant information and improving the efficiency of image segmentation. Experimental results revealed the model's good performance in various complex scenarios. However, the DRAN model is complex and requires considerable computational resources and training time. Additionally, DRAN is sensitive to the size and scale of input images and requires preprocessing and scaling before segmentation  
When comparing Tables 2 and 3, we found that image semantic segmentation technology based on deep learning has achieved great progress in accuracy. Each architecture has its unique advantages. With the common goal of using key technologies to improve segmentation accuracy, the future research direction in this field is the integration of the advantages of multiple architectures. In the process of implementing a deep neural network, accuracy evaluation may vary from problem to problem, and traditional semantic segmentation methods use accuracy or precision as a performance evaluation metric. However, for deep learning, many performance metrics are applicable to classification, object detection, and semantic segmentation. Typically, the final performance is evaluated in terms of accuracy, including pixel accuracy (PA), mean pixel accuracy (MPA), MIoU, and frequency weighted intersection over union (FWIoU). MIoU is often used to measure the performance of semantic segmentation models. It can be obtained by comparing the ground truth with the output map after passing the image to the export model. PA is the simplest pixel-level evaluation index in semantic segmentation. It only needs to calculate the ratio of correctly classified pixels in the image to the total pixels in the image, as shown in Formula (1):  
\[PA=\frac{\sum\limits_{i=0}^{n}p_{ii}}{\sum\limits_{i=0}^{n}\sum\limits_{j=0}^{n }p_{ij}} \tag{1}\]