Lane Detection Method Based on Semantic Segmentation and Road Structure

The accurate detection of lane markings plays a crucial role in the performance of intelligent assisted driving and lane departure warning systems. Current traditional research methods generally lack adaptability to complex road environments and need to improve detection accuracy. To address the problem of lane marking detection in complex traffic environments, a lane marking detection method based on semantic segmentation and road structure is proposed. The algorithm adopts an Encoder-Decoder network architecture to improve semantic segmentation. It uses the indexing function of pooling layers to perform upsampling in a de-convolutional manner, connecting multiple convolutional layers after each upsampling. The segmentation network is then trained using the standard cross-entropy loss function to obtain road segmentation images that exclude external environmental interference. Perspective transformation is applied to the segmented road images, and Hough transform and parameter space voting of edge points are used to quickly extract and correct the left and right boundary edge points of the lane markings. The extracted edge points are fitted using Bezier curves to achieve smooth display of the lane markings. The proposed algorithm was trained and tested on relevant lane marking datasets. Compared to the parameter space voting method, it achieved a 5.1% increase in accuracy with an average increase of 8 ms in time. Compared to the convolutional neural networks (CNN) network method, it had a 1.75% decrease in accuracy with an average decrease of 6.2 ms in time. The test results demonstrate that the proposed semantic segmentation encoding-decoding network helps optimize the model structure and reduces the demand for computing hardware resources while meeting real-time detection requirements.