深度学习在道路病害检测中的应用

Application of Deep Learning in Road Disease Detection

深度学习技术已被广泛用于道路病害检测。分析了相关算法、研究现状,并探讨了未来的发展方向,比较了2种主流的深度学习目标检测框架——Two-stage和One-stage,评估了它们的优势与局限。Two-stage框架(如RCNN、Fast R-CNN、Faster R-CNN)在检测精度上表现出色,但在实时处理方面存在局限;而One-stage框架(如YOLO和SSD)以其快速的检测速度见长,但在处理小目标和复杂背景时面临挑战。道路病害检测系统将进一步向智能化和自动化方向发展,依赖升级版的深度神经网络和多模态传感技术的深度融合,实现数据采集、病害识别、分类、定位和预测的全过程无人工干预,这为深度学习技术在道路病害检测领域的深入研究和应用提供了宝贵的参考,可进一步推动该领域的持续发展。

Deep learning technology has been widely used for road disease detection.Analyzed relevant algorithms,research status,and explored future development directions,compared two mainstream deep learning object detection frameworks-Two stage and One stage,and evaluated their advantages and limitations.The two-stage framework(such as R-CNN,Fast R-CNN,Faster R-CNN)performs well in detection accuracy,but has limitations in real-time processing.The One stage framework,such as YOLO and SSD,is known for its fast detection speed,but faces challenges when dealing with small targets and complex backgrounds.The road disease detection system will further develop towards intelligence and automation,relying on the deep integration of upgraded deep neural networks and multimodal sensing technology to achieve the entire process of data collection,disease recognition,classification,localization,and prediction without human intervention.This provides valuable reference for the in-depth research and application of deep learning technology in the field of road disease detection,and can further promote the sustainable development of this field.