路面
特征(语言学)
光学(聚焦)
坑洞(地质)
计算机科学
内涝(考古学)
人工智能
运输工程
工程类
地质学
土木工程
岩石学
哲学
物理
光学
生物
湿地
语言学
生态学
作者
Sharmad Bhat,Saish Naik,Mandar Gaonkar,Pradnya Sawant,Shailendra Aswale,Pratiksha Shetgaonkar
出处
期刊:2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE)
日期:2020-02-01
被引量:20
标识
DOI:10.1109/ic-etite47903.2020.67
摘要
Driverless or Autonomous cars are slowly catching the automobile market. Due to improper road management in India it becomes must to have a feature in the car which can detect and identify the cracked road and avoid the bad route and also updating the road condition. Roads are the simplest route to connect source to reach their destination. Due to natural causes like heavy rainfall, poor drainage, waterlogging and other factors such as the movement of heavily loaded vehicles are the frequent causes of road surface degradation. Crack and its types can be detected by a technique called as surface crack detection. In order to detect different cracks various methods and classifiers used are summarized in this paper. Traditionally road inspection is done manually which is time-consuming and costly. Instead of this automatic detection of cracks in the roads is preferred which can later be utilized as a feature in autonomous car. The focus of this paper is on different classifiers, for which various recent papers were studied and analyzed. Due to high accuracy and better performance CNN is highly preferred technique.
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