# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 5 Discussion
### Comparative Performance Analysis
#### 5.2.2 The Influence of Hardware  
For a concise comparative study, it is crucial that the experiments should be performed under the same conditions. In particular, the inference time is measured for a particular hardware configuration. As a result, the comparisons between the different architectures should be taken under deep examination, as the specific GPU model, the ram and other parameters play an important role in the efficiency which the models present. For this reason, the vast majority of the examined research papers provide information about the hardware specifications and the experiment conditions under which their proposed models \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Networks (Backbone)** & \multicolumn{3}{c}{**Cityscapes**} & \multicolumn{3}{c}{**CamVid**} \\
**mIoU (\%)** & **FPS** & **Hardware** & **mIoU (\%)** & **FPS** & **Hardware** \\ \hline SQ (SqueezeNet) & **84.3** & 16.7 & Jetson TX1 & - & - & - \\ DDRNet-23 & 79.4 & 38.5 & GTX 2080 Ti & **79.9** & 94 & GTX 2080 Ti \\ HyperSeg-S (EfficientNet-Bl) & 78.1 & 16.1 & GTX 1080 TI & 78.4 & 38.0 & GTX 1080 TI \\ DDRNet-23-film & 77.4 & 108.8 & GTX 2080 Ti & 78.0 & 230 & GTX 2080 Ti \\ LinkNet (ResNet18) & 76.4 & 18.7 & Titan X & - & - & - \\ U-HarDNet-70 (DenseNet) & 75.9 & 53 & GTX 1080 Ti & 67.7 & 149.3 & Titan V \\ HyperSeg-M (EfficientNet-Bl) & 75.8 & 36.9 & GTX 1080 Ti & - & - & - \\ SwiftNet-N (ResNet-18, & 75.5 & 39.9 & GTX 1080 Ti & 73.86 & 43.3 & GTX 1080 Ti \\ MobilNet V2) & 74.9 & 47.6 & Titan Xp & 74.8 & 59.2 & Titan Xp \\ ThellNet18 (ResNet, Xception, & 74.8 & 59.2 & GTX 1080 Ti & - & - & - \\ DenseNet) & 74.7 & 65.5 & Titan Xp & 68.7 & - & Titan Xp \\ SegBlocks-RN18 (t = 0.4) & 73.8 & 48.6 & GTX 1080 Ti & - & - & - \\ SS (ResNet18) & 72.9 & 14.7 & GTX 980 & - & - & - \\ BigNetV (VGGenet, Xception, & 72.6 & 156 & GTX 1080 Ti & 72.4 & 124.5 & GTX 1080 Ti \\ MobileNet, ShuffleNet) & 71.9 & **250.4** & GTX 1080 Ti & - & - & - \\ STDC1-50 (STDC) & 71.5 & 163.9 & GTX 1080 Ti & 71.1 & **398.1** & GTX 1080 Ti \\ FasterSeg (FasterSeg) & 71.5 & 163.9 & GTX 1080 Ti & 71.1 & **398.1** & GTX 1080 Ti \\ DFANet (Xception) & 71.3 & 100 & Titan X & 64.7 & 120 & Titan X \\ ESNet (ESNet) & 70.7 & 63 & GTX 1080 Ti & - & - & - \\ ICNet (image cascade network) & 70.6 & 30.3 & Titan X & 67.1 & 27.8 & Titan X \\ LEDNet (ResNetNet) & 70.6 & 71 & GTX 1080 Ti & - & - & - \\ STCD1-Seg (STDC) & - & - & - & 73.0 & 197.6 & GTX 1080 Ti \\ ERFNet (ERFNet) & 69.7 & 41.6 & Titan X & - & - & - \\ DSNet (DSNet) & 69.3 & 36.5 & GTX 1080 Ti & - & - & - \\ DABNet (DABNet) & 69.1 & 104.2 & GTX 1080 Ti & - & - & - \\ BSNet (Xception39) & 68.4 & 105.8 & Titan Xp & 68.7 & - & Titan Xp \\ ESSG (ERFNet) & 68 & - & - & - & - & - \\ Fast-SCNN (Fast-SCNN) & 68 & 123.5 & Nvidia Titan Xp & - & - & - \\ Template-Based-NAS-arch1 & 67.8 & 10 & GTX 1080 Ti & 63.2 & - & GTX 1080 Ti \\ (MobileNetV2) & 67.8 & 22 & GTX 1080 Ti & - & - & - \\ Template-Based-NAS-arch0 & 67.7 & 19 & GTX 1080 Ti & 63.9 & - & GTX 1080 Ti \\ (MobilNetV2) & 67.3 & 81.3 & Titan X & 66.4 & - & GTX 1080 Ti \\ ESPNetv2 (ESFNet) & 66.2 & 5.55 & GTX 1080 Ti & - & - & - \\ ContextNet (ContextNet) & 66.1 & 23.86 & Titan X & - & - & - \\ ENet + Lovasz-Softmax & 63.1 & 76.9 & - & - & - & - \\ ShuffleSeg (ShuffleNet) & 58.3 & 15.7 & Jetson TX2 & - & - & - \\ ENet (Enet, ResNets) & 58.3 & 46.8 & Titan X & 51.3 & - & - \\ LWRF (ResNet, MobileNetV2) & 45.8 & - & GTX 1080 Ti & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of real-time Semantic Segmentation models in “Cityscapes” and “CamVid” datasets. are being evaluated. To this end, at Table 5 the performance of each model is coupled with the GPU used during the experimental work. Beyond modern GPUs, fast inference could be achieved with other powerful computer devices. In fact, on the subject of the hardware choice [65] provide a thorough and concise list of the commercially available edge devices.