Intelligent Lane Line Detection for Unmanned Driving

In recent years, China's economy has grown rapidly, and the total number of automobiles has continued to rise, leading to deterioration of traffic conditions, intensified congestion, frequent accidents, and an increase in casualties and property losses. Therefore, research on unmanned driving systems for automobiles has gradually become a new trend. With technological progress and wide application, driverless vehicles will become an important part of daily life, promoting the transformation and upgrading of the transportation industry, driving the coordinated development of related industries, and injecting new vitality into economic growth. This paper expounds lane line detection based on deep learning. This method relies on models such as convolutional neural networks to extract and classify features from images. By training and learning the features of a large number of lane line images, it can accurately detect lane lines in new images. The lane line detection training model based on the PyTorch framework was adopted. After multiple trainings, the optimal model was tested and saved in the form of pictures and videos to simulate the effect of unmanned driving. The experimental results showed that the accuracy rate was over 90%.