GSBYOLO:一种轻量级的多尺度融合网络,用于复杂环境中的道路裂检测
Yuhao Wang1,2, Heran Zhu3, Yirong Wang1,2
1College of Water Conservancy and Hydropower, Sichuan Agricultural University, Yaan, Sichuan, China.
Scientific reports
|July 22, 2025
概括
本研究介绍了一种增强的GSB-YOLO模型,用于有效检测道路裂. 改进的模型提高了复杂环境中的检测准确性和稳定性,这对道路安全至关重要.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 道路裂检测对于交通安全和基础设施维护至关重要.
- 现有的方法在不同的裂纹尺度,大模型尺寸和复杂的背景下扎.
研究的目的:
- 为改善道路裂检测提出一个增强的GSB-YOLO模型.
- 解决当前方法的局限性,包括效率和稳定性.
主要方法:
- 开发了一个轻量级的网络结构,使用线性转换和长距离的注意力.
- 引入了一种新的SMC2f模块,用于在部结构中的动态神经元权重.
- 优化路径聚合网络 (PAN) 和双向特征金字塔网络 (BiFPN) 用于多级特征融合.
主要成果:
- 改进的GSB-YOLO模型显示了更高的检测效率和稳定性.
- 在道路裂检测中,平均平均精度 (mAP) 提高了3.2%.
- 成功应对不同目标规模和复杂背景的挑战.
结论:
- 拟议的GSB-YOLO模型在道路裂检测方面取得了重大进展.
- 该模型显示了确保道路和交通安全的实质性应用价值.
- 这项研究有助于建立更可靠的基础设施监控系统.
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