BL-YOLOv8:基于YOLOv8改进的道路缺陷检测模型
Xueqiu Wang1,2, Huanbing Gao1,2, Zemeng Jia1,2
1School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
这项研究介绍了BL-YOLOv8,这是一种用于道路缺陷检测的增强算法. 改进的模型实现了更高的准确性和效率,为自动道路监控和安全提供了有价值的解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 道路工程工程的道路工程.
背景情况:
- 手动检测道路缺陷是低效和昂贵的.
- 需要自动化系统,以便及时修复道路并确保道路安全.
- 现有的物体检测模型需要优化用于道路缺陷分析.
研究的目的:
- 开发一种基于YOLOv8s的增强道路缺陷检测算法BL-YOLOv8.
- 优化YOLOv8s模型以提高准确性,速度和减少计算负载.
- 与现有方法相比,验证拟议改进的有效性.
主要方法:
- 通过整合BiFPN概念,重建了YOLOv8s的部结构.
- 用SimSPPF模块优化了功能金字塔层,以提高速度.
- 引入了LSK注意力,一个动态的大卷积内核注意力机制,以提高检测准确度.
主要成果:
- 改进的BL-YOLOv8模型展示了有效地识别道路缺陷.
- 与原始模型相比,平均精度 (mAP@0.5) 提高了3.3%.
- 参数体积减少了29.92%和计算负载减少了11.45%.
结论:
- 拟议的BL-YOLOv8算法显著提高了道路缺陷检测性能.
- 这些优化导致了更高效,更准确的自动化道路检查系统.
- 这种方法为开发先进的自动道路缺陷检测系统提供了强有力的参考.
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