# Effects of Hybridizing the U-Net Neural Network in Traffic Lane Detection Process
## 4 Discussion  
The experimental results obtained show a clear improvement in performance with the introduction of the hybrid U-Net architecture, compared to the standard U-Net model (Tables 1 and 5, and Figures 8-11). On all three datasets used (TuSimple, Carla, and TuSimple+Carla), the hybrid U-Net achieved superior scores on both the mIoU and F1 score parameters. The most notable increase was observed on the mixed dataset TuSimple+Carla, where the mIoU increased from 0.56 to 0.60 (+7%) and the F1 score from 0.69 to 0.74 (+7.2%).  
The Figures 12-18 show how well the hybrid model performed in the variety of (mixed) data environments used. Similarly, on the Carla set--known for the complexity of images generated in simulated environments--hybrid U-Net achieved meaningful improvements, with an increase of +8.0% in mIoU and +5.7% in F1 score, indicating better adaptability to unconventional perspectives and geometric structures in complex images. However, these improvements came at a considerable computational cost.  
The inference time of the hybrid U-Net model was approximately 0.04 s per image, compared to 0.016 s per image for the standard U-Net, resulting in a decrease in FPS from 60 to approximately 23. This trade-off between accuracy and speed is essential to consider for real-time applications, where low latency is critical.  
While the accuracy improvements of \(\sim\)7% may appear modest in absolute terms, they represent meaningful gains in challenging lane detection scenarios where standard methods often fail. The performance improvements were particularly pronounced in complex conditions such as curved lanes, poor visibility, and geometric variations where our hybrid U-Net demonstrated almost doubled coverage metrics (1.1% vs. 0.6%) compared to baseline U-Net. However, the 2.5\(\times\) increase in computational cost does present challenges for real-time ADAS deployment, necessitating optimization strategies to maintain the accuracy benefits while reducing inference time to acceptable levels for automotive applications.  
The performance of the hybrid U-Net and U-Net neural networks was comparable when the lane lines were clearly marked and the markings were straight (Figure 13b.) The confidence level was the same for both models (0.04) and the coverage metric was higher for U-Net (by 0.4%) because the U-Net network was less precise. In the case of curves (Figures 15 and 16), the hybrid U-Net had significantly higher performance with a confidence level 0.02 higher in both cases and the coverage metric was almost double (1.1% vs. 0.6% and 1.0% vs. 0.5%, respectively).  
Hybrid U-Net had the greatest advantage over U-Net where the road markings were not clearly visible (Figure 14a,b). In the case of Figure 14b, all pixels detected by the U-Net network were below the confidence threshold (value of 0.5 for pixels viewed on the image). It is observed in Figures 12b and 15b that U-Net detected the marking line outside the roadway (see red arrows), and with the false detection the coverage metric was lower for U-Net by 0.2% compared to the efficiency of the proposed and developed hybrid model Hybrid U-Net.  
To better observe the capacity of the proposed hybrid U-Net network for future development, a comparative analysis of the performance of the networks used in traffic line detection is presented in Table 6.  
It should be noted that the values in the table should be considered with the caveat that the performance of a network depends greatly on the various settings and hardware resources used as well as on the number of data used for training and learning.