Application of Deep Learning in Road Disease Detection
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.
万方数据
维普
