# Effects of Hybridizing the U-Net Neural Network in Traffic Lane Detection Process
### Current Challenges and Research
To address this issue, a comprehensive dataset (NightLane) adapted for lane detection in low-light and multi-traffic conditions was introduced for the fused low-light enhancement framework (FLLENet) model [18]. Our testing on the NightLane and CULane datasets demonstrated superior performance in lane detection in low-light conditions. The results showed an F1 score of 76.90 on CULane and 78.91 on NightLane, demonstrating its effectiveness over other similar methods. Accurate and precise detection of traffic lanes and traffic signs is paramount for the safety and efficiency of autonomous vehicles and to address these issues, models need to be trained with a wide range of data samples for autonomous vehicles to perform well. In this regard, research has been conducted on less computationally complex reduced model variants (YOLOv8 nano). The YOLOv8n transfer learning models proposed in [19] show improved performance, with an average accuracy (mAP) of 90.6% and an inference speed of 117 frames per second (FPS) for traffic lane detection. Another study introduces an innovative ensemble learning technique (MAB-ensemble), designed for lane detection in road images for autonomous vehicles [20]. The proposed MAB-ensemble technique is based on multi-armed bandit optimization to facilitate efficient model selection for lane segmentation. The performance obtained under various conditions reached an overall accuracy of 90.28% in the dataset of 667 road images. In addition, its accuracy under day, night and abnormal road conditions reached 98.74%, 92.71%, and 79.38%, respectively, which demonstrates that the MAB-ensemble technique exhibits superior performance under all road conditions compared to individual CNN models and traditional ensemble techniques. Research has also been conducted on the effect of integrating an adaptive ROI selection module, an adaptive threshold adjustment module, and a color channel selection mechanism [21]. The proposed approach has demonstrated good results in addressing diverse and difficult weather conditions, the process of band detection. The proposed algorithm was evaluated on DSDLDE, Caltech, and TuSimple datasets and was compared with both geometry-based and learning-based methods. Overall, the proposed algorithm achieved demonstrably high levels of accuracy (>97%) in daytime cases, while nighttime results ranged from 92% to 99%. Among all weather conditions, the nighttime snowy scene was the most difficult (the worst results). A new lane detection method (E-CLNRet), based on the original CLRNet, was presented in [22]. The method integrates two additional components: the global feature optimizer (GFO) and the adaptive lane geometry aggregator (ALGA). The components were designed and integrated to improve the model's ability to handle complex road scenes (curves, shadows, and glare), improving the accuracy and stability of lane detection in difficult environments. As for the performance, in the curved lane line detection scenario, the F1 score of E-CLNRet improved by almost 3% compared to the original CLRNet. A lane detection model, based on a neural network that learns to decode the markings directly instead of relying on post-processing to define the lanes, is proposed in [23]. The method is based on a neural network designed to draw the detected lines on the recorded images, and thus the post-processing part becomes faster. The network training was performed on two datasets (images taken from the TuSimple and CULane databases), and the results obtained showed an accuracy of 95.2% on these datasets. A road marking detection system based on recorded images was proposed [24], by creating an additional layer of data that can be used by the automatic vehicle driving system. The authors used the YOLOv3 object detection algorithm, trained to recognize 25 different road markings using more than 25,000 images. The results obtained showed that this model had an accuracy of 98.28% and a detection speed from 20 msec to 41.05 msec.