Semantic segmentation is the problem of assigning a class label to every pixel in an image, and is an important component of an autonomous vehicle vision stack for facilitating scene understanding and object detection. However, many of the top performing semantic segmentation models are extremely complex and cumbersome, and as such are not suited to deployment onboard autonomous vehicle platforms where computational resources are limited and low-latency operation is a vital requirement. In this survey, we take a thorough look at the works that aim to address this misalignment with more compact and efficient models capable of deployment on low-memory embedded systems while meeting the constraint of real-time inference. We discuss several of the most prominent works in the field, placing them within a taxonomy based on their major contributions, and finally we evaluate the inference speed of the discussed models under consistent hardware and software setups that represent a typical research environment with high-end GPU and a realistic deployed scenario using low-memory embedded GPU hardware. Our experimental results demonstrate that many works are capable of real-time performance on resource-constrained hardware, while illustrating the consistent trade-off between latency and accuracy.
翻译:语义分解是给图像中的每个像素指定一个类标签的问题,是自动车辆视觉堆积的重要组成部分,有助于了解现场和探测物体。然而,许多最精致的语义分解模型极其复杂和繁琐,因此不适合在自动车辆平台上部署,在这些平台上,计算资源有限,低纬度操作是一项至关重要的要求。在本次调查中,我们透彻地审视了旨在解决这种与更紧凑、效率更高的模型不匹配的工程,这些模型能够在满足实时推断限制的同时,在低模嵌入系统上部署,同时满足实时内嵌的制约。我们讨论了该领域的一些最突出的工程,根据它们的主要贡献将其置于分类中,最后,我们根据它们的主要贡献,评估了讨论过的模型的推论速度,这些模型在连贯的硬件和软件组合下代表一种典型的研究环境,具有高端的GPU,而使用低模嵌入式的GPU硬件是现实的部署情景。我们的实验结果表明,许多工程能够实时地在资源紧缺的硬件上进行操作,同时说明固定和精确之间的交易。