# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 4 Comparison of Selected AI Models
### Semantic Segmentation  
Semantic segmentation is the process of assigning a label to each pixel in an image so that pixels with the same label have some common characteristics. In the case of the Cityscapes dataset, semantic segmentation involves assigning labels (e.g., \"tree\", \"car\", \"sidewalk\", etc.) to pixels in images of urban scenes (Figure 2). Models glassed using data from Cityscapes generate predictions that can be compared to benchmark data.  
Table 4 presents a comparative analysis of various semantic segmentation models on the Cityscapes dataset, evaluated in terms of mean Intersection over Union (mIoU) and frames per second (fps) on different GPUs. The analysis highlights both segmentation accuracy and real-time performance.  
Among the models evaluated in Table 4 for their semantic segmentation proficiency on Cityscapes images, YOLOv8-DSAF and RT-DETR stand out prominently. YOLOv8-DSAF  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **mIoU** & **Fps (GPU)** & **Year** & **Ref.** \\ \hline YOLOv8-DSAF & 96.1\% & 192 (3090) & 2024 & [129] \\ RT-DETR & 89.1\% & 200 (3090) & 2023 & [130] \\ YOLOv8n & 87.2\% & 178 (3090) & 2023 & [131] \\ VLTSeg & 86.4\% & 76 (n/a) & 2023 & [132] \\ PIDNet-L & 80.6\% & 31.1 (3090) & 2023 & [133] \\ SFNet-R18 & 80.4\% & 25.7 (1080 Ti) & 2020 & [134] \\ PIDNet-M & 79.8\% & 42.2 (3090) & 2022 & [133] \\ PIDNet-S & 78.6\% & 93.2 (3090) & 2022 & [133] \\ RegSeg & 78.3\% & 30 (n/a) & 2021 & [135] \\ PP-LiteSeg-B2 & 77.5\% & 102.6 (1080 Ti) & 2022 & [136] \\ DDRNet-23-lim & 77.4\% & 101.6 (2080 Ti) & 2021 & [137] \\ DPNet & 76.8\% & 20 (n/a) & 2023 & [138] \\ (DeepLabV3 Plus) & 76.8\% & 97.0 (1080 Ti) & 2021 & [139] \\ STDC2-75 & 76.8\% & 97.0 (1080 Ti) & 2021 & [139] \\ U-HarDNet-70 & 75.9\% & 53 (1080 Ti) & 2019 & [140] \\ HyperSeg-M & 75.8\% & 36.9 (n/a) & 2020 & [141] \\ SwiftNetRN-18 & 75.5\% & 39.9 (n/a) & 2019 & [142] \\ STDC1-75 & 75.3\% & 126.7 (n/a) & 2021 & [139] \\ BiSeNet V2-Large & 75.3\% & 47.3 (n/a) & 2021 & [143] \\ TD4-BISE18 & 74.9\% & 47.6 (Titan X) & 2020 & [144] \\ PP-LiteSeg-T2 & 74.9\% & 143.6 (1080 Ti) & 2022 & [136] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of selected models capable of semantic segmentation tested on a set of images obtained from Cityscapes.  
Figure 2: An example of semantic segmentation on the Cityscapes set where: (**a**) original image; (**b**) ground truth image; and (**c**) model prediction result [128].  
achieves the highest mIoU (mean Intersection over Union) of 96.1%, which indicates its superior accuracy in segmenting images. Additionally, it operates at an impressive rate of 192 fps on an NVIDIA 3090 GPU, making it not only highly accurate, but also exceptionally fast. This model uses three key modules: Depthwise Separatable Convolution (DSConv), a Dual-Path Attention Gate (DPAG), and a Feature Enhancement Module (FEM). With these technologies, YOLOv8-DSAF achieves high detection accuracy in dynamic urban scenarios. RT-DETR, although slightly less accurate with an mIoU of 89.1%, surpasses YOLOv8-DSAF in speed, achieving 200 fps on the same GPU, which makes it notable for real-time applications. RT-DETR is the first fully end-to-end real-time object detector that eliminates the need for Non-Maximum Suppression (NMS). Moreover, models like YOLOv8n and VLTSeg also show high mIoU scores of 87.2% and 86.4%, respectively, but they fall behind in terms of fps, with YOLOv8n operating at 178 fps and VLTSeg at a significantly lower 76 fps. These metrics highlight YOLOv8-DSAF and RT-DETR as the most efficient and effective models in terms of both segmentation accuracy and processing speed.  
This comparative analysis underscores the progressive enhancement in semantic segmentation capabilities, reflecting improvements in both mIoU and fps over time, driving the efficiency and applicability of these models in real-world scenarios.