Abstract:

In order to ensure the safe driving of vehicles at night, the night lane lines were accurately recognized and lane departure warnings were made, a deep generative network EnhanceGAN for nighttime image enhancement and an end-to-end lane line detection network AttentiveLSTR based on Transformer were proposed for nighttime lane line detection, and experiments with real vehicles were conducted. The deep generative network EnhanceGAN used the improved UNet as the generator of the network, adopted a two-layer nested U-shape structure to expand the sensory field, and added a Markov local discriminator and a combined loss function to enhance the detailed information of lane line edges and textures. The lane line detection network AttentiveLSTR used ResNeXt as a feature extraction network to ensure the network depth and reduced the number of model parameters, and introduced feature pyramid networks (FPN) to extract lane line edge and shape information. The results show that compared with the mainstream methodsCycleGAN and Gamma Correction, the pro[osed method is more effective in nighttime image enhancement on the BDD100k dataset, with a high contrast between lane lines and surrounding environment, structural similarity (SSIM) of 0.883 4, natural and realistic images as a whole, peak signal-to-noise ratio (PSNR) of 40.265 4, and natural image quality evaluation index (NIQE) of 3.423 3; the detection accuracy (Acc) on the CULane dataset is 90.12%, and the processing speed is fast, with 82 frames per second (FPS). The research results can provide a reference for nighttime lane line deviation scenarios.

Key words: intelligent driving, automotive safety, generation network, night scenes, lane detection, deep learning