Lane Line Recognition Image Dataset

In the current transportation industry, with the rapid development of autonomous driving technology, the ability to recognize lane lines in real-time, accurately, and reliably is key to vehicle safety. However, current solutions still lack accuracy in complex environments, especially when road markings are blurred or obscured. Our dataset primarily addresses the precision issue of recognizing lane lines in complex environments to meet the business needs of real driving scenarios. Data collection is conducted using high-definition cameras mounted on different types of vehicles, sampling under various weather, time, and road conditions. Quality control includes multiple rounds of annotation and expert review, with each image independently annotated by at least three experienced annotators and subjected to consistency checks. The team consists of 15 experts in traffic engineering and image processing. Data preprocessing involves steps such as grayscale transformation, contrast enhancement, etc., all stored in standardized formats to facilitate machine learning algorithm applications.