改进车道检测模型，让自动驾驶更准识别复杂车道线
Cross Dataset Analysis and Network Architecture Repair for Autonomous Car Lane Detection
- 提出ERFCondLaneNet架构，优化复杂车道线检测
- 在两个数据集上表现接近原模型，参数量减少46%
- 适合关注自动驾驶感知与模型轻量化研究者
迁移学习已成为解决孤立学习范式的标准方法，通过利用一个任务中获取的知识来解决相关任务。然而，在应用迁移学习前，仍需研究其初始步骤以提升验证与可解释性。本研究针对自动驾驶车辆的车道检测任务，开展了跨数据集分析与网络结构修复。车道检测是自动驾驶辅助系统的关键环节。尽管当前基于深度学习的车道识别系统在多数情况下表现良好，但在复杂拓扑车道（如密集、弯曲、分叉）上仍存在困难。提出的ERFCondLaneNet是对CondlaneNet框架的改进，旨在提升复杂车道线的检测能力。该方法在CULane和CurveLanes两个常用基准上进行测试，采用ResNet和ERFNet两种主干网络。实验结果表明，ERFCondLaneNet在性能与ResnetCondLaneNet相当的同时，特征使用量减少33%，模型大小降低46%。
原文摘要 · Abstract (English)
Transfer Learning has become one of the standard methods to solve problems to overcome the isolated learning paradigm by utilizing knowledge acquired for one task to solve another related one. However, research needs to be done, to identify the initial steps before inducing transfer learning to applications for further verification and explainablity. In this research, we have performed cross dataset analysis and network architecture repair for the lane detection application in autonomous vehicles. Lane detection is an important aspect of autonomous vehicles driving assistance system. In most circumstances, modern deep-learning-based lane recognition systems are successful, but they struggle with lanes with complex topologies. The proposed architecture, ERFCondLaneNet is an enhancement to the CondlaneNet used for lane identification framework to solve the difficulty of detecting lane lines with complex topologies like dense, curved and fork lines. The newly proposed technique was tested on two common lane detecting benchmarks, CULane and CurveLanes respectively, and two different backbones, ResNet and ERFNet. The researched technique with ERFCondLaneNet, exhibited similar performance in comparison to ResnetCondLaneNet, while using 33% less features, resulting in a reduction of model size by 46%.
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