基于MS-YOLOv8的物体检测方法用于路面疾病
Zhibin Han1, Yutong Cai1, Anqi Liu1
1School of Transportation, Jilin University, Changchun 130022, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
这项研究介绍了MS-YOLOv8,这是一种用于检测路面疾病的改进算法. 它提高了道路维护的准确性和适应性,提供了更有效的自动化解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 土木工程 土木工程是指土木工程.
背景情况:
- 检测路面疾病对于道路维护至关重要.
- 传统的方法是低效和不准确的.
- 需要自动检测系统.
研究的目的:
- 介绍MS-YOLOv8,一种增强的路面疾病识别算法.
- 提高检测准确度和适应不同路面条件的适应性.
- 为道路缺陷检测提供自动化解决方案.
主要方法:
- 修改了YOLOv8模型,采用了三个新的机制:可变形的大核注意力 (DLKA),可分离的大核注意力 (LSKA) 和具有空间加权扩展卷积 (SWDA) 的多尺度扩展注意力.
- 对于多尺度目标,DLKA可以动态调整卷积内核.
- LSKA增强了特征提取,SWDA提高了背景区分和精度.
主要成果:
- 通过MS-YOLOv8,背景分类的准确度提高了6%.
- 整体精度提高了1.9%,平均精度 (mAP) 提高了1.4%.
- 特定疾病检测mAP在可比检测速度下增加了2.9%.
结论:
- MS-YOLOv8显著提高了路面疾病检测的准确性和适应性.
- 新的注意力机制提高了多尺度特征提取和精度.
- 这个算法为自动化道路缺陷检测系统提供了有价值的参考.
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