# Effects of Hybridizing the U-Net Neural Network in Traffic Lane Detection Process
## 5 Conclusions  
The research topic addressed in this article is a topic of immediate interest in the field of design, development, and validation of solutions that can directly contribute to the development of ADAS systems and implicitly to the reduction of the risks of road accidents. By developing and implementing models and methods in the structure of a standard network architecture (U-Net), the hybrid U-Net model was developed, which offers high robustness and precision in the process of identifying traffic lines. It should be noted that in the case of the U-Net + TE architecture, the weakest results were obtained because the datasets used (TuSimple--3626 images, Carla--3890 images and the mixed set TuSimple + Carla--3500 images) do not offer a large enough volume to fully exploit the capabilities of TE. These architectures, known for their ability to capture global relationships and complex spatial contexts, usually require larger and more varied datasets to achieve superior performance. In comparison, U-Net (having a simpler and more efficient architecture) is easier to train on reduced volumes of data and manages to generalize better in this context. Thus, the slightly weaker performances of the U-Net + TE model can be explained by the fact that it did not have enough data to effectively learn advanced contextual representations. This aspect suggests that a future research direction could aim at training TE models on larger or augmented datasets, in order to explore their full potential. The use of pre-training or fine-tuning methods on external datasets can also be considered to improve performance in data-limited scenarios. Future research should investigate knowledge distillation techniques to address the computational efficiency limitations observed in our hybrid U-Net model. While the proposed architecture achieved superior accuracy (+7.2% F1 score improvement), the inference time increased from 16 ms to 40 ms, limiting real-time ADAS deployment. A teacher-student framework, where the full hybrid U-Net serves as teacher and a compact variant (hybrid U-Net-Lite) as student, could potentially reduce model parameters by 40% while retaining > 95% of the accuracy benefits. This approach would preserve the synergistic effects of our integrated modules (GSC, TE, MSLDM) while achieving the target inference time of \(\sim\)25 ms required for real-time lane detection at highway speeds. Additionally, complementary optimization strategies such as INT8 quantization and hardware-specific acceleration could further enhance deployment feasibility without compromising the architectural innovations that enable superior performance in complex lane detection scenarios. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multicolumn{4}{c}{**Performance Indicators**} \\ \cline{2-5}  & **mIoU** & **F1 Score** & **Inference Time** & **FPS** \\  & & & **[Msec/Frame]** & **FPS** \\ \hline U-Net & 0.80 & 0.83–0.87 & 50 & 20 \\ SCNN & 0.77 & 0.72–0.80 & 60 & 7.5–16.7 \\ PolyLane Net & 0.74 & 0.53–0.78 & 45 & 22.2 \\ YOLO & 0.79 & 0.82 & 30–69.9 & 33.3 \\ ResNet34 & - & 0.71–0.96 & - & 280 \\ U-Net (our dataset) & 0.56 & 0.69 & 16 & 60 \\ Hybrid U-Net & 0.60 & 0.69–0.74 & 40 & 28.82 \\ (our dataset) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative analysis of performance parameters for different traffic line detection networks, reported in [36; 37; 38; 39; 40; 41; 42]. **Author Contributions:** Conceptualization, A.C. and F.M. ; methodology, A.C. and F.M. ; software, A.C.; validation, A.C. and F.M. ; investigation, A.C.; writing--original draft preparation, A.C. and F.M. ; writing--review and editing, A.C. and F.M. All authors have read and agreed to the published version of the manuscript. **Funding:** The APC was funded by Technical University of Cluj-Napoca. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.