实时检测自动驾驶中的未知物体，提升行车安全。
UNCOVER: Unknown Class Object Detection for Autonomous Vehicles in Real-time
- 通过预测占据区域来评估物体存在性，不依赖具体类别。
- 在真实驾驶数据集上实现高召回率，同时保持实时性能。
- 结合深度图几何信息过滤误检，尤其减少近距离误报。
自动驾驶系统运行在开放世界场景中，必然面临未知物体。然而，基于有限基础类训练的标准检测器会忽略未知物体，带来潜在风险。为此，我们提出从训练中看到的物体学习通用的物体存在性，而非特定类别。方法包括预测占据区域并计算实际物体占据比例以评分物体存在性。为增强泛化能力，利用Mosaic和Mixup数据增强引入其他领域数据以增加物体多样性。非自动驾驶训练类别的物体被归为新增的分布外（OOD）类。所提方案UNCOVER在挑战性自动驾驶基准上实现了实时检测与高召回率的未知物体检测。为进一步降低误报率，尤其是近距离物体，引入后处理过滤步骤，利用深度图提取几何线索，该线索通常可在自动驾驶系统中获取。
原文摘要 · Abstract (English)
Autonomous driving (AD) operates in open-world scenarios, where encountering unknown objects is inevitable. However, standard object detectors trained on a limited number of base classes tend to ignore any unknown objects, posing potential risks on the road. To address this, it is important to learn a generic rather than a class specific objectness from objects seen during training. We therefore introduce an occupancy prediction together with bounding box regression. It learns to score the objectness by calculating the ratio of the predicted area occupied by actual objects. To enhance its generalizability, we increase the object diversity by exploiting data from other domains via Mosaic and Mixup augmentation. The objects outside the AD training classes are classified as a newly added out-of-distribution (OOD) class. Our solution UNCOVER, for UNknown Class Object detection for autonomous VEhicles in Real-time, excels at achieving both real-time detection and high recall of unknown objects on challenging AD benchmarks. To further attain very low false positive rates, particularly for close objects, we introduce a post-hoc filtering step that utilizes geometric cues extracted from the depth map, typically available within the AD system.
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