改进特征融合与注意力机制，提升道路裂缝检测精度。
Enhancing Road Crack Detection Accuracy with BsS-YOLO: Optimizing Feature Fusion and Attention Mechanisms
- 优化双向特征金字塔与路径聚合网络，增强多尺度特征融合
- 引入加权融合与通道-空间注意力，检测mAP提升2.8%
- 适合城市道路与高速公路的智能巡检应用
有效的道路裂缝检测对道路安全、基础设施维护及延长道路寿命至关重要，具有显著经济效益。然而，现有方法在目标尺度差异大、背景复杂以及环境适应性差方面仍存在挑战。本文提出BsS-YOLO模型，通过改进路径聚合网络（PAN）和双向特征金字塔网络（BiFPN）实现多尺度特征融合优化，并引入加权特征融合机制，提升特征表示能力，增强检测准确率与鲁棒性。此外，在主干网络中集成简单有效的注意力机制（SimAM），通过空间与通道注意力提升定位精度；检测层融合通道重排与混合的混洗注意力机制（Shuffle Attention），重构关键特征表示，进一步提升性能。实验表明，该模型在道路裂缝检测上实现了2.8%的mAP提升，适用于城市道路养护与高速公路巡检等多种场景。
原文摘要 · Abstract (English)
Effective road crack detection is crucial for road safety, infrastructure preservation, and extending road lifespan, offering significant economic benefits. However, existing methods struggle with varied target scales, complex backgrounds, and low adaptability to different environments. This paper presents the BsS-YOLO model, which optimizes multi-scale feature fusion through an enhanced Path Aggregation Network (PAN) and Bidirectional Feature Pyramid Network (BiFPN). The incorporation of weighted feature fusion improves feature representation, boosting detection accuracy and robustness. Furthermore, a Simple and Effective Attention Mechanism (SimAM) within the backbone enhances precision via spatial and channel-wise attention. The detection layer integrates a Shuffle Attention mechanism, which rearranges and mixes features across channels, refining key representations and further improving accuracy. Experimental results show that BsS-YOLO achieves a 2.8% increase in mean average precision (mAP) for road crack detection, supporting its applicability in diverse scenarios, including urban road maintenance and highway inspections.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。