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WA-YOLO:一个基于YOLOv8的爆炸现场的爆炸物检测算法
LinNa Li1,2,3, Han Gao1, JunYi Lu1
1College of Science, Wuhan University of Science and Technology, Wuhan, Hubei, China.
这项研究介绍了WA-YOLO,这是一个改进的火器检测算法,用于爆炸安全. 该模型在复杂环境中增强了特征提取和物体检测的准确性,大大提高了引爆器检测率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 安全工程安全工程
背景情况:
- 烟火技术检测对于爆炸安全至关重要,但受到复杂环境和不规则物体外观的挑战.
- 现有的方法在不同的物体尺寸和不规则的引爆线的姿势方面扎.
研究的目的:
- 开发一个先进的物体检测算法,以改善爆炸安全的烟火检测.
- 为了提高检测小型和不规则形状的目标 (如引爆线) 的准确性和稳定性.
主要方法:
- 拟议的WA-YOLO算法集成波形可分离卷积 (WSDConv) 和多尺度并行注意力机制.
- 集成Wise-IoU损失功能,以提高不规则形状的界限框精度.
- 在子网络内修改过的交叉阶段部分 (CSP) 结构,用于多层次物体检测.
主要成果:
- 在定制的爆破数据集上,平均精度提高了12.6%,引爆器检测得到了8.3%的改进.
- 在VOC2012数据集上表现出更好的表现,显示1.3%的回忆率和1.6%的平均精度增加.
- WA-YOLO模型在不同的数据集和复杂场景中表现出强烈的概括性.
结论:
- WA-YOLO算法提供了一个有效的解决方案,用于在具有挑战性的爆炸环境中检测烟火和引爆器.
- 波纹卷积和注意力机制的整合增强了特征提取和多尺度检测能力.
- 该模型的强度和通用性能使其适合于现实世界的安全应用.
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