论文标题
关键点估计和点实例分割方法用于车道检测
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
论文作者
论文摘要
自主驾驶的感知技术应适应各种环境。在交通线检测的情况下，基本的感知模块应考虑许多条件，例如目标系统的交通线数量和计算能力。为了解决这些问题，在本文中，我们提出了一种称为点实例网络（Pinet）的流量线检测方法；该方法基于关键点估计和实例分割方法。 Pinet包括几个同时训练的堆叠的沙漏网络。因此，可以根据目标环境的计算能力选择受过训练的模型的大小。我们将预测的关键点作为实例分割问题提出了聚类问题；无论交通线数量如何，都可以训练Pinet。 pinet在Tusimple和Culane数据集上实现了竞争精度，并呈误报，这是流行的公共数据集用于车道检测。我们的代码可从https://github.com/koyeegngmin/pinet_new获得
Perception techniques for autonomous driving should be adaptive to various environments. In the case of traffic line detection, an essential perception module, many condition should be considered, such as number of traffic lines and computing power of the target system. To address these problems, in this paper, we propose a traffic line detection method called Point Instance Network (PINet); the method is based on the key points estimation and instance segmentation approach. The PINet includes several stacked hourglass networks that are trained simultaneously. Therefore the size of the trained models can be chosen according to the computing power of the target environment. We cast a clustering problem of the predicted key points as an instance segmentation problem; the PINet can be trained regardless of the number of the traffic lines. The PINet achieves competitive accuracy and false positive on the TuSimple and Culane datasets, popular public datasets for lane detection. Our code is available at https://github.com/koyeongmin/PINet_new
