MPR-net: Multi-scale key points regression for lane detection

Lane detection is usually regarded as a semantic segmentation task, however, segmentation-based methods require expensive computational costs, and it is difficult to segment the lanes with heavy noises. Therefore, this paper proposes a lane detection method based on multi-scale key point regression, which directly uses the position of the key points of the lane instead of predicting the pixel-wise outputs. First, we design a lightweight backbone to extract a series of feature maps with a forward view image as input, and then apply a multi-scale fusion network on these feature maps to obtain the location and confidence information of the key points of the lane. Finally, a clustering and curve fitting mechanism with quadratic inverse proportion are used to obtain the final lane detection. Our proposed model can recognize dashed lane markings and handle many extreme scenarios where lanes are completely occluded or heavily noised. In addition, our model uses a relatively explicit framework, which contributes to ensuring real-time performance at 30Hz. In order to prove our method's performance, we conduct experiments on the TuSimple benchmark and RVD dataset, and results demonstrate that our method achieves competitive results compared with other methods.