  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.
