Abstract This paper presents a multi-task instance segmentation neural network able to provide both road lane and road participants detection. The multi-tastk approach, ERFNet-based, allows feature sharing and reduces the computational requirements of the overall detection architecture, allowing real time performance even in configurations with limited hardware. The proposed method includes an ad-hoc training procedure and automatic dataset creation mechanism that is also introduced in this paper. The proposed solution has been tested and validated through a newly generated public dataset derived from the BDD100K of 19K images, and in real scenarios. The results obtained prove the viability of the work for road application and its real time performance.

中文翻译：

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使用卷积神经网络对道路对象和车道进行联合语义分割

摘要 本文提出了一种多任务实例分割神经网络，能够提供道路车道和道路参与者检测。基于 ERFNet 的多任务方法允许特征共享并降低整体检测架构的计算要求，即使在硬件有限的配置中也能实现实时性能。所提出的方法包括临时训练程序和本文中还介绍的自动数据集创建机制。所提出的解决方案已经通过从 19K 图像的 BDD100K 派生的新生成的公共数据集在实际场景中进行了测试和验证。获得的结果证明了道路应用工作的可行性及其实时性能。