Lane line detection method based on semi-supervised generative adversarial network

Abstract

The invention provides a lane line detection method based on a semi-supervised generative adversarial network. The lane line detection method comprises the steps of: S1, constructing the generative adversarial network, and establishing a training set, a verification set and a test set of the generative adversarial network; S2, pre-training the generative adversarial network through utilizing labeled data in the training set; S3, performing real training on the generative adversarial network by using the labeled data and the unlabeled data in the training set, and adjusting hyper-parameters ofthe generative adversarial network in a real training process through using the verification set; S4, after the real training is finished, evaluating the generalization ability of the generative adversarial network through using the test set, and if the generalization ability reaches a preset standard, entering S5; and S5, inputting an actual street image into a generator network subjected to realtraining to obtain an actual lane line of the actual street image, and superposing the actual lane line on the actual street image to complete lane line detection. According to the lane line detection method, the lane line identification precision can be improved.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a lane line detection method based on a semi-supervised generation confrontation network. Background technique [0002] Traditional lane line detection methods rely on a combination of highly specialized, hand-crafted features and heuristics to identify lane segments. These include color-based features, structure tensors, bar filters, ridge features, etc., which may be combined with Hough transforms and particle or Kalman filters. After the lane segments are identified, post-processing techniques are used to filter out false detections, and the segments are combined to form the final result lane. In general, these traditional methods are prone to robustness issues due to changes in road scenes, which are difficult to model for such model-based systems. Therefore, the traditional lane line detection method is difficult to apply to the environment where the real-time detection effe...

AI Technical Summary

Problems solved by technology

The optimization process of GAN is a process of maximal and minimal game. Through training, the Nash equilibrium is finally reached, so that the generator can estimate the potential distribution of the data, and the discriminator cannot distinguish the real image from the generated image.

Images