The paper presents CLRmatchNet, an integrated model that incorporates MatchNet into the existing state-of-the-art lane detection network, CLRNet. The key highlights are:
MatchNet is a deep learning-based submodule that replaces the conventional classical cost function-based label assignment process in CLRNet. MatchNet learns to optimally assign predictions to ground truth (GT) lanes during training.
The integration of MatchNet significantly improves the detection of curved lanes, achieving +2.8% for ResNet34, +2.3% for ResNet101, and +2.96% for DLA34 on the CULane dataset's curve category, while maintaining comparable performance in other test scenarios.
MatchNet boosts the confidence levels of true positive lane detections, enabling the adjustment of the confidence score threshold used in the evaluation process to further improve performance.
The dynamic selection of the number of prediction matches per GT, based on MatchNet's scores, optimizes the matching process.
The authors also fine-tuned CLRNet on a curated curved lane subset of the training data, further enhancing the model's performance on curved lanes when combined with MatchNet.
Overall, the proposed CLRmatchNet approach demonstrates the potential of deep learning-based label assignment methods to address the limitations of classical cost functions, particularly in challenging scenarios like curved lane detection.
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by Sapir Konten... klo arxiv.org 04-02-2024
https://arxiv.org/pdf/2309.15204.pdfSyvällisempiä Kysymyksiä