Abstract
The pavement inspection task, which mainly includes crack and garbage detection, is essential and carried out frequently. The human-based or dedicated system approach for inspection can be easily carried out by integrating with the pavement sweeping machines. This work proposes a deep learning-based pavement inspection framework for self-reconfigurable robot named Panthera. Semantic segmentation framework SegNet was adopted to segment the pavement region from other objects. Deep Convolutional Neural Network (DCNN) based object detection is used to detect and localize pavement defects and garbage. Furthermore, Mobile Mapping System (MMS) was adopted for the geotagging of the defects. The proposed system was implemented and tested with the Panthera robot having NVIDIA GPU cards. The experimental results showed that the proposed technique identifies the pavement defects and litters or garbage detection with high accuracy. The experimental results on the crack and garbage detection are presented. It is found that the proposed technique is suitable for deployment in real-time for garbage detection and, eventually, sweeping or cleaning tasks.
Highlights
IntroductionThe development of urban pavement infrastructure systems is an integral part of modern city expansion processes
This section describe the experimental results of proposed system
The detection or segmentation performance of DNN relies on various parameters, including the size of the training data set, data-set class balance, illumination conditions, and hyperparameters
Summary
The development of urban pavement infrastructure systems is an integral part of modern city expansion processes. The pavement infrastructure has been growing multiple folds due to developing new communities and sustainable transport initiatives. Maintaining a defects free, clean, and hygienic pavement environment is a vital yet formidable Pavement Management System (PMS) task. I.e., identifying defects and litter or garbage with cleaning, are mandatory to achieve a defects-free and hygienic pavement environment. In PMS, human inspectors are widely used for defect and cleanness inspection. This method takes a long inspection time and needs a qualified expert to systematically record the severity of defects and mark defects’ spatial location. Routine cleaning of lengthy pavement is a tedious task for sanitary workers
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