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Perception techniques for autonomous driving should be adaptive to variousenvironments. In the case of traffic line detection, an essential perceptionmodule, many condition should be considered, such as number of traffic linesand computing power of the target system. To address these problems, in thispaper, we propose a traffic line detection method called Point Instance Network(PINet); the method is based on the key points estimation and instancesegmentation approach. The PINet includes several stacked hourglass networksthat are trained simultaneously. Therefore the size of the trained models canbe chosen according to the computing power of the target environment. We cast aclustering problem of the predicted key points as an instance segmentationproblem; the PINet can be trained regardless of the number of the trafficlines. The PINet achieves competitive accuracy and false positive on theTuSimple and Culane datasets, popular public datasets for lane detection. Ourcode is available at https://github.com/koyeongmin/PINet_new

Key Points Estimation and Point Instance Segmentation Approach for Lane Detection