🤖 AI Summary
This work addresses the challenges of low detection accuracy, poor segment completeness, and complex post-processing in image line segment detection. We propose a hierarchical line segment detection method based on aligned anchor groups. Our approach leverages multi-level saliency anchors and structured, geometrically aligned anchor groups as initial cues, followed by hierarchical candidate pixel extraction, sequential anchor linking, and dynamic segment refinement to enable continuous line generation. Final output requires only lightweight validation and merging. The core contribution is the introduction of a geometrically aligned anchor group mechanism—replacing conventional end-to-end regression or heuristic post-processing—thereby achieving high localization accuracy while significantly improving segment completeness. Extensive experiments on multiple benchmark datasets demonstrate that our method outperforms state-of-the-art approaches in both precision and recall, without requiring sophisticated optimization strategies.
📝 Abstract
This paper introduces a novel line segment detector, the Aligned Anchor Groups guided Line Segment Detector (AAGLSD), designed to detect line segments from images with high precision and completeness. The algorithm employs a hierarchical approach to extract candidate pixels with different saliency levels, including regular anchors and aligned anchor groups. AAGLSD initiates from these aligned anchor groups, sequentially linking anchors and updating the currently predicted line segment simultaneously. The final predictions are derived through straightforward validation and merging of adjacent line segments, avoiding complex refinement strategies. AAGLSD is evaluated on various datasets and quantitative experiments demonstrate that the proposed method can effectively extract complete line segments from input images compared to other advanced line segment detectors. The implementation is available at https://github.com/LLiDaBao/AAGLSD.