End-to-End Lane detection with One-to-Several Transformer

• URL: http://arxiv.org/abs/2305.00675v4
• Date: Sat, 13 May 2023 04:42:42 GMT
• Title: End-to-End Lane detection with One-to-Several Transformer
• Authors: Kunyang Zhou and Rui Zhou
• Abstract summary: O2SFormer converges 12.5x faster than DETR for the ResNet18 backbone. O2SFormer with ResNet50 backbone achieves 77.83% F1 score on CULane dataset, outperforming existing Transformer-based and CNN-based detectors.
• Score: 6.79236957488334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although lane detection methods have shown impressive performance in real-world scenarios, most of methods require post-processing which is not robust enough. Therefore, end-to-end detectors like DEtection TRansformer(DETR) have been introduced in lane detection.However, one-to-one label assignment in DETR can degrade the training efficiency due to label semantic conflicts. Besides, positional query in DETR is unable to provide explicit positional prior, making it difficult to be optimized. In this paper, we present the One-to-Several Transformer(O2SFormer). We first propose the one-to-several label assignment, which combines one-to-many and one-to-one label assignment to solve label semantic conflicts while keeping end-to-end detection. To overcome the difficulty in optimizing one-to-one assignment. We further propose the layer-wise soft label which dynamically adjusts the positive weight of positive lane anchors in different decoder layers. Finally, we design the dynamic anchor-based positional query to explore positional prior by incorporating lane anchors into positional query. Experimental results show that O2SFormer with ResNet50 backbone achieves 77.83% F1 score on CULane dataset, outperforming existing Transformer-based and CNN-based detectors. Futhermore, O2SFormer converges 12.5x faster than DETR for the ResNet18 backbone.

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