With modern science and technology development, vehicles are equipped with intelligent driver assistant systems, of which lane detection is a key function. These complex detection structures (either wide or deep) are investigated to boost the accuracy and overcome the challenges in complicated scenarios. However, the computation and memory storage cost will increase sharply, and the response time will also increase. For resource-constrained devices, lane detection networks with a low cost and short inference time should be implemented. To get more accurate lane detection results, the large (deep and wide) detection structure is framed for high-dimensional and highly robust features, and deep supervision loss is applied on different resolutions and stages. Despite the high-precision advantage, the large detection network cannot be used for embedded devices directly because of the demand for memory and computation. To make the network thinner and lighter, a general training strategy, called self-knowledge distillation (SKD), is proposed. It is different from classical knowledge distillation; there are no independent teacher-student networks, and the knowledge is distilled itself. To evaluate more comprehensively and precisely, a new lane data set is collected. The Caltech Lane date set and TuSimple lane data set are also used for evaluation. Experiments further prove that a small student network and large teacher network have a similar detection accuracy via SKD, and the student network has a shorter inference time and lower memory usage. Thus it can be applied for resource-limited devices flexibly.

中文翻译：

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通过自我知识蒸馏来训练狭窄和浅色车道检测网络

随着现代科学技术的发展，车辆配备了智能驾驶辅助系统，其中车道检测是关键功能。研究了这些复杂的检测结构（宽或深），以提高准确性并克服复杂场景中的挑战。但是，计算和内存存储成本将急剧增加，响应时间也会增加。对于资源受限的设备，应实现成本低，推理时间短的车道检测网络。为了获得更准确的车道检测结果，对大型（深和宽）检测结构进行了构架，以实现高维度和高度鲁棒的功能，并在不同的分辨率和阶段应用了深度监控损失。尽管有高精度优势，由于对内存和计算的需求，大型检测网络无法直接用于嵌入式设备。为了使网络更薄更轻，提出了一种称为自知识蒸馏（SKD）的通用培训​​策略。它不同于经典知识的提炼。没有独立的师生网络，知识本身就是提炼的。为了更全面，更准确地评估，收集了一个新的车道数据集。加州理工学院车道日期集和TuSimple车道数据集也用于评估。实验进一步证明，小型学生网络和大型教师网络通过SKD具有相似的检测精度，并且该学生网络具有较短的推理时间和较低的内存使用率。因此，它可以灵活地应用于资源受限的设备。