[论文解读] Salient Sign Detection In Safe Autonomous Driving: AI Which Reasons Over Full Visual Context

Ross Greer, Akshay Gopalkrishnan+3 more|arXiv (Cornell University)|Jan 14, 2023

Safety Warnings and Signage被引用 7

一句话总结

论文提出一个关注对驾驶员决策有影响的交通标志的显著性感知检测框架，利用新的 LAVA 显著性标志数据集和显著性敏感 focal loss 来训练 Deformable DETR，以提升对显著标志的召回率。

ABSTRACT

Detecting road traffic signs and accurately determining how they can affect the driver's future actions is a critical task for safe autonomous driving systems. However, various traffic signs in a driving scene have an unequal impact on the driver's decisions, making detecting the salient traffic signs a more important task. Our research addresses this issue, constructing a traffic sign detection model which emphasizes performance on salient signs, or signs that influence the decisions of a driver. We define a traffic sign salience property and use it to construct the LAVA Salient Signs Dataset, the first traffic sign dataset that includes an annotated salience property. Next, we use a custom salience loss function, Salience-Sensitive Focal Loss, to train a Deformable DETR object detection model in order to emphasize stronger performance on salient signs. Results show that a model trained with Salience-Sensitive Focal Loss outperforms a model trained without, with regards to recall of both salient signs and all signs combined. Further, the performance margin on salient signs compared to all signs is largest for the model trained with Salience-Sensitive Focal Loss.

研究动机与目标

• 通过关注对驾驶员决策有意义的标志来推动安全的自动驾驶。
• 定义交通标志在驾驶场景中的显著性属性并构建带有显著性标签的数据集（LAVA 显著性标志数据集）。
• 开发在模型训练中强调显著标志的损失函数，以提升可行动检测。

提出的方法

• 在驾驶场景中定义交通标志的显著性属性。
• 创建带有显著性标签的 LAVA 显著性标志数据集。
• 使用显著性敏感 focal loss 训练 Deformable DETR 目标检测器，以强调显著标志。
• 评估显著标志的召回率和对所有标志的召回率。

实验结果

研究问题

• RQ1 显著性感知损失是否能在不降低非显著标志性能的情况下提升对显著标志的检测召回率？
• RQ2 通过显著性感知框架融合完整视觉上下文是否能提升对安全自动驾驶标志的理解？
• RQ3 显著性敏感 focal loss 与标准损失在检测显著标志方面有何差异？
• RQ4 在所提出方法下，显著标志的召回率相对于所有标志的召回率有何影响？

主要发现

• 使用显著性敏感 focal loss 训练的模型在显著标志和所有标志的召回率方面均优于非显著性基线。
• 在显著性感知损失下，显著标志相对于所有标志的性能差距更大。
• 该方法利用 Deformable DETR 检测器以利用完整视觉上下文进行显著性推理。