With the continuous advancement of intelligent transportation systems

lane detection technology plays a crucial role in enhancing road safety and promoting the development of autonomous driving. This study focuses on implementing an efficient and accurate CNN-based lane detection algorithm to address the challenges posed by complex and variable road environments. By leveraging the powerful pattern recognition capabilities of deep learning

we have developed a customized CNN model capable of accurately detecting lane markings under different lighting conditions

weather situations

and road surface qualities.In this project

I adopted the TuSimple lane detection dataset and preprocessed it to meet the requirements for model training. I chose YOLO as the base model architecture and made optimization adjustments to ensure the effectiveness and generalization ability of the model. Extensive experimental validation was also performed to confirm these aspects.Experimental results show that the proposed CNN model performs as expected in lane detection tasks

achieving high-precision detection results while maintaining good real-time processing performance. The model exhibits robustness when facing harsh weather and complex road conditions. Additionally

by comparing the performance differences under various parameter configurations

we further explored the key factors influencing model performance.In summary

this study presents a novel and effective solution for lane detection. Future work will be dedicated to exploring more improvement strategies

such as integrating multi-sensor information fusion

to achieve higher detection accuracy and broader application scope.