用无人机图像自动检测混凝土桥缺陷，靠显著性定位异常区域
Saliency-Guided Deep Learning for Bridge Defect Detection in Drone Imagery
- 先用显著性分析定位缺陷候选区，再增强其亮度进行检测
- 在标准数据集上准确率高，处理速度快，适合实时巡检系统
- 适用于桥梁智能巡检、基础设施运维等场景
异常目标检测与分类是计算机视觉与模式识别中的主要挑战之一。本文提出一种新方法，利用无人机影像自动检测、定位并分类混凝土桥结构缺陷。该框架包含两个阶段：第一阶段基于显著性生成缺陷区域候选，因缺陷常表现出与周围正常表面模式的局部不连续性；第二阶段采用基于YOLOX的深度学习检测器，在对显著缺陷区域进行边界框级亮度增强后处理的图像上运行。标准数据集上的实验结果证实了本框架在准确性和计算效率方面的优异表现，展现出在自供电巡检系统中大规模应用的巨大潜力。
原文摘要 · Abstract (English)
Anomaly object detection and classification are one of the main challenging tasks in computer vision and pattern recognition. In this paper, we propose a new method to automatically detect, localize and classify defects in concrete bridge structures using drone imagery. This framework is constituted of two main stages. The first stage uses saliency for defect region proposals where defects often exhibit local discontinuities in the normal surface patterns with regard to their surrounding. The second stage employs a YOLOX-based deep learning detector that operates on saliency-enhanced images obtained by applying bounding-box level brightness augmentation to salient defect regions. Experimental results on standard datasets confirm the performance of our framework and its suitability in terms of accuracy and computational efficiency, which give a huge potential to be implemented in a self-powered inspection system.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。