基于YOLOv8的统一驾驶感知中频率感知任务加权探索性研究
An Exploratory Study of Frequency-Aware Task Weighting for YOLOv8-Based Unified Driving Perception
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中文总结 AI 辅助
本研究在基于YOLOv8的统一驾驶感知框架中探索频率感知任务加权（FTW），通过损失历史低频能量比动态平衡目标检测、可行驶区域分割和车道分割任务，实验表明该方法可行但未确立优于基线。
中文摘要 AI 辅助
统一感知使自动驾驶系统能够在单个网络内执行目标检测、可行驶区域分割和车道分割，从而提高效率并降低部署复杂性。联合优化多个感知任务仍然具有挑战性，因为任务表现出不同的收敛速度、损失尺度和优化稳定性。现有的任务加权方法使用损失幅度、学习的不确定性、短期损失变化或梯度统计；在此，我们探索近期损失历史窗口的频率结构作为补充信号。我们实现并研究了频率感知任务加权（FTW），这是一种动态任务平衡规则，它根据近期损失历史的低频能量比来估计损失轨迹稳定性代理。FTW为那些均值中心化损失轨迹包含较大比例低频功率的任务分配更大的权重。我们在基于统一YOLOv8的感知框架（具有三个任务特定头）下，记录了FTW和两个基线在全网络静态训练和渐进冻结下的表现。在Mapillary Vistas上的实验比较了FTW与固定加权和基于不确定性的加权在两种配置下的性能。最终保留指标针对每次运行中每轮验证损失最低的检查点进行报告。在六个单次运行配置中，静态FTW具有最大的综合得分和车道mIoU，渐进FTW具有最大的检测mAP，静态不确定性加权具有最大的可行驶区域mIoU。由于没有重复种子估计、单任务基线或FTW消融，这些排名是描述性的。证据支持基于损失频率的加权在该流程中的可行性，但并未确立相对于基线的改进或超出所报告运行的泛化性。
英文摘要
Unified perception enables autonomous driving systems to perform object detection, drivable-area segmentation, and lane segmentation within a single network, improving efficiency and reducing deployment complexity. Jointly optimizing multiple perception tasks remains challenging because tasks exhibit different convergence rates, loss scales, and optimization stability. Existing task-weighting methods use loss magnitude, learned uncertainty, short-term loss changes, or gradient statistics; here, we explore the frequency structure of a recent loss-history window as a complementary signal. We implement and examine Frequency-aware Task Weighting (FTW), a dynamic task-balancing rule that estimates a loss-trajectory stability proxy from the low-frequency energy ratio of recent loss histories. FTW assigns larger weights to tasks whose mean-centered loss trajectories contain a larger proportion of low-frequency power. We document FTW and two baselines under full-network static training and progressive freezing using a unified YOLOv8-based perception framework with three task-specific heads. Experiments on Mapillary Vistas compare FTW with fixed and uncertainty-based weighting under both configurations. Final holdout metrics are reported for the checkpoint with the lowest per-epoch validation loss in each run. Across six single-run configurations, static FTW has the largest derived overall score and lane mIoU, progressive FTW has the largest detection mAP, and static uncertainty weighting has the largest drivable-area mIoU. Without repeated-seed estimates, single-task baselines, or FTW ablations, these rankings are descriptive. The evidence supports the feasibility of loss-frequency-based weighting in this pipeline, but does not establish improvement over the baselines or generalization beyond the reported runs.
发表机构
- NYU Shanghai（上海纽约大学）
机构由 AI 辅助整理，请以论文原文为准。
