CFI-Former: Efficient lane detection by multi-granularity perceptual query attention transformer

Abstract

Benefiting from the booming development of Transformer methods, the performance of lane detection tasks has been rapidly improved. However, due to the influence of inaccurate lane line shape constraints, the query sequences of existing transformer-based lane line detection methods contain a large number of repetitive and invalid information regions, which leads to redundant information in the detection region and makes the processing of information on localized feature details of the lanes biased. In this paper, a multi-granularity perceptual query attention transformer lane detection method, CFI-Former, is proposed to achieve more accurate lane detection. Specifically, a multi-granularity perceptual query attention (GQA) module is designed to extract lane local detail information. By a two-stage query from coarse to fine, redundant key–value pairs with low information relevance are first filtered out, and then fine-grained token-to-token attention is executed on the remaining candidate regions. This module emphasizes the multi-granularity nuances of lane features from global to local, leading to more effective models based on lane line shape constraints. In addition, weighted adaptive LIoU loss ($L_{\phi -LI\text{oU}}$) is proposed to improve lane detection in more challenging scenarios by adaptively increasing the relative gradient of high IoU lane objects and the weight of the loss. Extensive experiments show that CFI-Former outperforms the baseline on two popular lane detection benchmark datasets.