选择性聚焦：研究车道检测训练后量化中的语义敏感性
Selective Focus: Investigating Semantics Sensitivity in Post-training Quantization for Lane Detection
AI总结：
针对车道检测PTQ中物理语义输出易受量化误差影响的问题，提出Selective Focus框架，通过语义引导聚焦和敏感性感知选择提升量化性能。
摘要：
车道检测（LD）在增强自动驾驶的L2+能力方面发挥着关键作用，受到了广泛关注。训练后量化（PTQ）可以促进LD模型的实际应用，在无需标注数据的情况下实现高速运行和有限内存占用。然而，以往的PTQ方法没有考虑包含偏移量、位置等物理语义的复杂LD输出，因此无法直接应用于LD模型。本文中，我们开创性地研究了车道检测后处理的语义敏感性，并提出了一种新的车道失真分数。此外，我们识别出影响量化后LD性能的两个主要因素，即头内敏感性和头间敏感性，其中特定语义中的微小量化误差可能导致显著的车道失真。因此，我们提出了一个部署有语义引导聚焦和敏感性感知选择模块的选择性聚焦框架，以将后处理信息纳入PTQ重建。基于观察到的头内敏感性，引入语义引导聚焦，使用实用代理优先处理前景相关语义。针对头间敏感性，我们提出敏感性感知选择，在运行时高效识别有影响力的预测头并细化优化目标。我们在包括基于关键点、锚点、曲线和分割的多种模型上进行了大量实验。我们的方法可在单块GPU上数分钟内生成量化模型，并在CULane数据集上实现6.4%的F1分数提升。
英文摘要：
Lane detection (LD) plays a crucial role in enhancing the L2+ capabilities of autonomous driving, capturing widespread attention. The Post-Processing Quantization (PTQ) could facilitate the practical application of LD models, enabling fast speeds and limited memories without labeled data. However, prior PTQ methods do not consider the complex LD outputs that contain physical semantics, such as offsets, locations, etc., and thus cannot be directly applied to LD models. In this paper, we pioneeringly investigate semantic sensitivity to post-processing for lane detection with a novel Lane Distortion Score. Moreover, we identify two main factors impacting the LD performance after quantization, namely intra-head sensitivity and inter-head sensitivity, where a small quantization error in specific semantics can cause significant lane distortion. Thus, we propose a Selective Focus framework deployed with Semantic Guided Focus and Sensitivity Aware Selection modules, to incorporate post-processing information into PTQ reconstruction. Based on the observed intra-head sensitivity, Semantic Guided Focus is introduced to prioritize foreground-related semantics using a practical proxy. For inter-head sensitivity, we present Sensitivity Aware Selection, efficiently recognizing influential prediction heads and refining the optimization objectives at runtime. Extensive experiments have been done on a wide variety of models including keypoint-, anchor-, curve-, and segmentation-based ones. Our method produces quantized models in minutes on a single GPU and can achieve 6.4% F1 Score improvement on the CULane dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
