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DAEiS-Net:深度聚合网络与边缘信息补充,用于道水污点细分
Yuliang Wang1,2,3, Kai Huang4, Kai Zheng5
1Beijing Metro Construction Administration Co., Ltd., Beijing 100068, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
检测道水污点对于城市基础设施安全至关重要. 深度聚合网络与边缘信息补充 (DAEiS-Net) 方法通过增强特征提取和边缘信息来提高准确性,优于现有的方法.
科学领域:
- 城市工程是城市工程.
- 土木工程 土木工程是指土木工程.
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 检测和维护道疾病对于城市交通安全至关重要.
- 在道中检测水污点是具有挑战性的,因为不同的形态,规模,以及失去上下文和边界信息.
- 现有的方法在复杂的道环境中与多尺度的特征提取和边缘细节作斗争.
研究的目的:
- 提出一种新的深度学习方法,DAEiS-Net,用于准确检测道水污点.
- 增强多尺度上下文和边缘信息的提取,以提高细分精度.
- 引入一个新的数据集,TWS,用于训练和评估道水污点细分模型.
主要方法:
- 该研究介绍了带有边缘信息补充的深度聚合网络 (DAEiS-Net),使用编码器解码器架构.
- 关键模块包括用于功能增强的深度聚合模块 (DAM),用于噪声抑制和纹理增强的多尺度交叉注意模块 (MCAM),以及用于弥合语义差距的边缘信息补充模块 (EISM).
- 一个子像素模块 (SPM) 用于多级特征融合和边缘表示增强.
主要成果:
- DAEiS-Net在新推出的道水污染数据集 (TWS) 上,在道水污染细分方面表现出卓越的性能.
- 拟议的模块有效地解决了与多尺度信息提取和边界细节相关的挑战.
- 实验结果证实了DAEiS-Net对于这个特定任务的最先进能力.
结论:
- DAEiS-Net在自动化道水污点检测和细分方面取得了重大进展.
- 该方法能够整合多尺度特征和边缘信息,从而获得更强大,更准确的结果.
- TWS数据集为未来探测道缺陷的研究提供了宝贵的资源.
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