坑洞(地质)
加速度计
计算机科学
卷积神经网络
背景(考古学)
全球定位系统
陀螺仪
路面
振动
实时计算
人工智能
遥感
工程类
电信
地质学
岩石学
土木工程
物理
量子力学
航空航天工程
操作系统
古生物学
作者
Furkan ÖZOĞLU,Türkay Gökgöz
出处
期刊:Sensors
[MDPI AG]
日期:2023-11-07
卷期号:23 (22): 9023-9023
被引量:5
摘要
In the context of road transportation, detecting road surface irregularities, particularly potholes, is of paramount importance due to their implications for driving comfort, transportation costs, and potential accidents. This study presents the development of a system for pothole detection using vibration sensors and the Global Positioning System (GPS) integrated within smartphones, without the need for additional onboard devices in vehicles incurring extra costs. In the realm of vibration-based road anomaly detection, a novel approach employing convolutional neural networks (CNNs) is introduced, breaking new ground in this field. An iOS-based application was designed for the acquisition and transmission of road vibration data using the built-in three-axis accelerometer and gyroscope of smartphones. Analog road data were transformed into pixel-based visuals, and various CNN models with different layer configurations were developed. The CNN models achieved a commendable accuracy rate of 93.24% and a low loss value of 0.2948 during validation, demonstrating their effectiveness in pothole detection. To evaluate the performance further, a two-stage validation process was conducted. In the first stage, the potholes along predefined routes were classified based on the labeled results generated by the CNN model. In the second stage, observations and detections during the field study were used to identify road potholes along the same routes. Supported by the field study results, the proposed method successfully detected road potholes with an accuracy ranging from 80% to 87%, depending on the specific route.
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