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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.","2026-02-04 17:25:39","2026-10-19 17:40:29","0.00",708,0,500,"lane line recognition, autonomous driving dataset, intelligent transportation images","High-precision lane line recognition image dataset, suitable for autonomous driving research.","JPG","1.3G","autonomous driving, intelligent transportation, road monitoring","image recognition, pattern recognition, deep 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Width","lane_width","Indicates the width of the lane line, measured in meters.","float",{"id":611,"name":612,"field":613,"intro":614,"type":609},7484,"Lane Curvature","lane_curvature","Indicates the curvature of the lane line, describing the degree of bend.",{"id":616,"name":617,"field":618,"intro":619,"type":590},7485,"Road Condition","road_condition","Indicates the current condition of the road, such as dry, slippery, or snowy.",{"id":621,"name":622,"field":623,"intro":624,"type":590},7486,"Weather Condition","weather_condition","Indicates the weather condition at the time of image capture, such as sunny, rainy, or foggy.",{"id":626,"name":627,"field":628,"intro":629,"type":590},7487,"Light Condition","light_condition","Indicates the lighting condition at the time of image capture, such as daytime or nighttime.",{"id":631,"name":632,"field":633,"intro":634,"type":590},7488,"Traffic Density","traffic_density","Indicates the traffic density in the image, such as free-flow or congested.",[636,640,644,648,652],{"id":637,"question":638,"answer":639},5296,"What is the main purpose of the lane line recognition image dataset?","The dataset is mainly used to support lane line detection and recognition in autonomous vehicles, as well as to improve the accuracy and efficiency of lane line detection in intelligent transportation systems.",{"id":641,"question":642,"answer":643},5297,"Which transportation driving technologies is this dataset suitable for?","The lane line recognition image dataset is suitable for lane line detection and recognition technologies in autonomous driving, advanced driver assistance systems (ADAS), and traffic monitoring systems.",{"id":645,"question":646,"answer":647},5298,"What are the possible application scenarios for utilizing the lane line recognition image dataset?","This dataset can be used for developing lane-keeping systems in autonomous vehicles, dynamic lane recommendation in real-time traffic management systems, and lane marking detection and maintenance by road maintenance departments.",{"id":649,"question":650,"answer":651},5299,"How to efficiently utilize the lane line recognition image dataset for model training?","To improve the efficiency of model training, the dataset can be augmented through image rotation, scaling, and color variation to enhance the model's robustness to lane lines under different lighting and weather conditions.",{"id":653,"question":654,"answer":655},5300,"What is the relationship between the lane line recognition image dataset and intelligent transportation systems?","The lane line recognition image dataset provides a foundation for the development and optimization of lane detection and prediction modules within intelligent transportation systems by offering high-quality lane line annotated 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