This paper provides a report on our solution including model selection, tuning strategy and results obtained for Global Road Damage Detection Challenge. This Big Data Cup Challenge was held as a part of IEEE International Conference on Big Data 2020. We assess single and multi-stage network architectures for object detection and provide a benchmark using popular state-of-the-art open-source PyTorch frameworks like Detectron2 and Yolov5. Data preparation for provided Road Damage training dataset, captured using smartphone camera from Czech, India and Japan is discussed. We studied the effect of training on a per country basis with respect to a single generalizable model. We briefly describe the tuning strategy for the experiments conducted on two-stage Faster R-CNN with Deep Residual Network (Resnet) and Feature Pyramid Network (FPN) backbone. Additionally, we compare this to a one-stage Yolov5 model with Cross Stage Partial Network (CSPNet) backbone. We show a mean F1 score of 0.542 on Test2 and 0.536 on Test1 datasets using a multi-stage Faster R-CNN model, with Resnet-50 and Resnet-101 backbones respectively. This shows the generalizability of the Resnet-50 model when compared to its more complex counterparts. Experiments were conducted using Google Colab having K80 and a Linux PC with 1080Ti, NVIDIA consumer grade GPU. A PyTorch based Detectron2 code to preprocess, train, test and submit the Avg F1 score to is made available at https://github.com/vishwakarmarhl/rdd2020
Universidad Autónoma de Guadalajara（瓜达拉哈拉自治大学）；Vidrona LTD（Vidrona有限公司）；ITESM Campus Guadalajara（蒙特雷科技大学瓜达拉哈拉校区）
Research on damage detection of road surfaces has been an active area of re-search, but most studies have focused so far on the detection of the presence of damages. However, in real-world scenarios, road managers need to clearly understand the type of damage and its extent in order to take effective action in advance or to allocate the necessary resources. Moreover, currently there are few uniform and openly available road damage datasets, leading to a lack of a common benchmark for road damage detection. Such dataset could be used in a great variety of applications; herein, it is intended to…
Federal University of Santa Catarina（圣卡塔琳娜联邦大学）；National Institute for Digital Convergence（国家数字融合研究所）
A challenge still to be overcome in the field of visual perception for vehicle and robotic navigation on heavily damaged and unpaved roads is the task of reliable path and obstacle detection. The vast majority of the researches have as scenario roads in good condition, from developed countries. These works cope with few situations of variation on the road surface and even fewer situations presenting surface damages. In this paper we present an approach for road detection considering variation in surface types, identifying paved and unpaved surfaces and also detecting damage and other informati…
This paper summarizes the design, experiments and results of our solution to the Road Damage Detection and Classification Challenge held as part of the 2018 IEEE International Conference On Big Data Cup. Automatic detection and classification of damage in roads is an essential problem for multiple applications like maintenance and autonomous driving. We demonstrate that convolutional neural net based instance detection and classfication approaches can be used to solve this problem. In particular we show that Mask-RCNN, one of the state-of-the-art algorithms for object detection, localization a…
Research on damage detection of road surfaces using image processing techniques has been actively conducted, achieving considerably high detection accuracies. Many studies only focus on the detection of the presence or absence of damage. However, in a real-world scenario, when the road managers from a governing body need to repair such damage, they need to clearly understand the type of damage in order to take effective action. In addition, in many of these previous studies, the researchers acquire their own data using different methods. Hence, there is no uniform road damage dataset available…
Lamar University（拉马尔大学）；Stanley Black & Decker（史丹利百得）
Pavement condition evaluation is essential to time the preventative or rehabilitative actions and control distress propagation. Failing to conduct timely evaluations can lead to severe structural and financial loss of the infrastructure and complete reconstructions. Automated computer-aided surveying measures can provide a database of road damage patterns and their locations. This database can be utilized for timely road repairs to gain the minimum cost of maintenance and the asphalt's maximum durability. This paper introduces a deep learning-based surveying scheme to analyze the image-based d…
Road damage detection is critical for the maintenance of a road, which traditionally has been performed using expensive high-performance sensors. With the recent advances in technology, especially in computer vision, it is now possible to detect and categorize different types of road damages, which can facilitate efficient maintenance and resource management. In this work, we present an ensemble model for efficient detection and classification of road damages, which we have submitted to the IEEE BigData Cup Challenge 2020. Our solution utilizes a state-of-the-art object detector known as You O…
Classifying and counting vehicles in road traffic has numerous applications in the transportation engineering domain. However, the wide variety of vehicles (two-wheelers, three-wheelers, cars, buses, trucks etc.) plying on roads of developing regions without any lane discipline, makes vehicle classification and counting a hard problem to automate. In this paper, we use state of the art Convolutional Neural Network (CNN) based object detection models and train them for multiple vehicle classes using data from Delhi roads. We get upto 75% MAP on an 80-20 train-test split using 5562 video frames …
StripRFNet: A Strip Receptive Field and Shape-Aware Network for Road Damage Detection
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院数字地球科学重点实验室）；International Research Center of Big Data for Sustainable Development Goals（可持续发展大数据国际研究中心）；University of Chinese Academy of Sciences（中国科学院大学）；Southeast University（东南大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）
Timely disaster risk management requires accurate road maps and prompt damage assessment. Currently, this is done by volunteers manually marking satellite imagery of affected areas but this process is slow and often error-prone. Segmentation algorithms can be applied to satellite images to detect road networks. However, existing methods are unsuitable for disaster-struck areas as they make assumptions about the road network topology which may no longer be valid in these scenarios. Herein, we propose a CNN-based framework for identifying accessible roads in post-disaster imagery by detecting ch…
Durham University（杜伦大学）；Institute for Infocomm Research（资讯通信研究所）
Scene understanding for autonomous vehicles is a challenging computer vision task, with recent advances in convolutional neural networks (CNNs) achieving results that notably surpass prior traditional feature driven approaches. However, limited work investigates the application of such methods either within the highly unstructured off-road environment or to RGBD input data. In this work, we take an existing CNN architecture designed to perform semantic segmentation of RGB images of urban road scenes, then adapt and retrain it to perform the same task with multichannel RGBD images obtained unde…
The ability to automatically detect other vehicles on the road is vital to the safety of partially-autonomous and fully-autonomous vehicles. Most of the high-accuracy techniques for this task are based on R-CNN or one of its faster variants. In the research community, much emphasis has been applied to using 3D vision or complex R-CNN variants to achieve higher accuracy. However, are there more straightforward modifications that could deliver higher accuracy? Yes. We show that increasing input image resolution (i.e. upsampling) offers up to 12 percentage-points higher accuracy compared to an of…
University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）
The detrimental impacts of climate change include stronger and more destructive hurricanes happening all over the world. Identifying different damaged structures of an area including buildings and roads are vital since it helps the rescue team to plan their efforts to minimize the damage caused by a natural disaster. Semantic segmentation helps to identify different parts of an image. We implement a novel self-attention based semantic segmentation model on a high resolution UAV dataset and attain Mean IoU score of around 88% on the test set. The result inspires to use self-attention schemes in…
Towards a safe and comfortable driving, road scene segmentation is a rudimentary problem in camera-based advance driver assistance systems (ADAS). Despite of the great achievement of Convolutional Neural Networks (CNN) for semantic segmentation task, the high computational efforts of CNN based methods is still a challenging area. In recent work, we proposed a novel approach to utilise the advantages of CNNs for the task of road segmentation at reasonable computational effort. The runtime benefits from using irregular super pixels as basis for the input for the CNN rather than the image grid, w…
BSN iNet Co. Ltd.（BSN iNet 有限公司）；Tokyo National Museum（东京国立博物馆）；Universiti Teknologi PETRONAS（马来西亚石油科技大学）
This study evaluates road surface object detection tasks using four Mask R-CNN models as a pre-study of surface deterioration detection of stone-made archaeological objects. The models were pre-trained and fine-tuned by COCO datasets and 15,188 segmented road surface annotation tags. The quality of the models were measured using Average Precisions and Average Recalls. Result indicates substantial number of counts of false negatives, i.e. left detection and unclassified detections. A modified confusion matrix model to avoid prioritizing IoU is tested and there are notable true positive increase…
Tongji University（同济大学）；Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）；the Hong Kong University of Science and Technology（香港科技大学）；SmartMore（思谋科技）；University of Thessaloniki（塞萨洛尼基大学）
Existing road pothole detection approaches can be classified as computer vision-based or machine learning-based. The former approaches typically employ 2-D image analysis/understanding or 3-D point cloud modeling and segmentation algorithms to detect road potholes from vision sensor data. The latter approaches generally address road pothole detection using convolutional neural networks (CNNs) in an end-to-end manner. However, road potholes are not necessarily ubiquitous and it is challenging to prepare a large well-annotated dataset for CNN training. In this regard, while computer vision-based…
The University of Tokyo（东京大学）；Urban-X Technologies, Inc.（Urban-X科技公司）
Road rutting is a severe road distress that can cause premature failure of road incurring early and costly maintenance costs. Research on road damage detection using image processing techniques and deep learning are being actively conducted in the past few years. However, these researches are mostly focused on detection of cracks, potholes, and their variants. Very few research has been done on the detection of road rutting. This paper proposes a novel road rutting dataset comprising of 949 images and provides both object level and pixel level annotations. Object detection models and semantic …
Since convolutional neural network(CNN)models emerged,several tasks in computer vision have actively deployed CNN models for feature extraction. However,the conventional CNN models have a high computational cost and require high memory capacity, which is impractical and unaffordable for commercial applications such as real-time on-road object detection on embedded boards or mobile platforms. To tackle this limitation of CNN models, this paper proposes a wide-residual-inception (WR-Inception) network, which constructs the architecture based on a residual inception unit that captures objects of …
Indian Institute of Technology Jodhpur（印度理工学院焦特布尔分校）
Predicting on-road abnormalities such as road accidents or traffic violations is a challenging task in traffic surveillance. If such predictions can be done in advance, many damages can be controlled. Here in our wok, we tried to formulate a solution for automated collision prediction in traffic surveillance videos with computer vision and deep networks. It involves object detection, tracking, trajectory estimation, and collision prediction. We propose an end-to-end collision prediction system, named as COLLIDE-PRED, that intelligently integrates the information of past and future trajectories…
Maintaining the roadway infrastructure is one of the essential factors in enabling a safe, economic, and sustainable transportation system. Manual roadway damage data collection is laborious and unsafe for humans to perform. This area is poised to benefit from the rapid advance and diffusion of artificial intelligence technologies. Specifically, deep learning advancements enable the detection of road damages automatically from the collected road images. This work proposes to collect and label road damage data using Google Street View and use YOLOv7 (You Only Look Once version 7) together with …
North Carolina State University（北卡罗来纳州立大学）；Colaberry Inc（Colaberry公司）；Iowa State University（爱荷华州立大学）
Severe weather events can cause large financial losses to farmers. Detailed information on the location and severity of damage will assist farmers, insurance companies, and disaster response agencies in making wise post-damage decisions. The goal of this study was a proof-of-concept to detect damaged crop areas from aerial imagery using computer vision and deep learning techniques. A specific objective was to compare existing object detection algorithms to determine which was best suited for crop damage detection. Two modes of crop damage common in maize (corn) production were simulated: stalk…
UC San Diego（加州大学圣迭戈分校）；HKUST Robotics Institute（香港科技大学机器人研究所）
Freespace detection is an essential component of visual perception for self-driving cars. The recent efforts made in data-fusion convolutional neural networks (CNNs) have significantly improved semantic driving scene segmentation. Freespace can be hypothesized as a ground plane, on which the points have similar surface normals. Hence, in this paper, we first introduce a novel module, named surface normal estimator (SNE), which can infer surface normal information from dense depth/disparity images with high accuracy and efficiency. Furthermore, we propose a data-fusion CNN architecture, referre…
Advanced Research Lab, NavInfo Europe（四维图新欧洲高级研究实验室）
Road infrastructure maintenance inspection is typically a labor-intensive and critical task to ensure the safety of all road users. Existing state-of-the-art techniques in Artificial Intelligence (AI) for object detection and segmentation help automate a huge chunk of this task given adequate annotated data. However, annotating videos from scratch is cost-prohibitive. For instance, it can take an annotator several days to annotate a 5-minute video recorded at 30 FPS. Hence, we propose an automated labelling pipeline by leveraging techniques like few-shot learning and out-of-distribution detect…
TD-RD: A Top-Down Benchmark with Real-Time Framework for Road Damage Detection
University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Alipurduar Government Engineering and Management College（阿里布尔杜阿尔政府工程与管理学院）；MBZUAI（Mohamed bin Zayed 人工智能大学）；Carnegie Mellon University（卡内基梅隆大学）
Accurate automated detection of road pavement distresses is critical for the timely identification and repair of potentially accident-inducing road hazards such as potholes and other surface-level asphalt cracks. Deployment of such a system would be further advantageous in low-resource environments where lack of government funding for infrastructure maintenance typically entails heightened risks of potentially fatal vehicular road accidents as a result of inadequate and infrequent manual inspection of road systems for road hazards. To remedy this, a recent research initiative organized by the …
Neighborhood Attention Makes the Encoder of ResUNet Stronger for Accurate Road Extraction
Simon Fraser University（西蒙弗雷泽大学）；Jalpaiguri Government Engineering College（贾尔派古里政府工程学院）；University of Waterloo（滑铁卢大学）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（亥姆霍兹德累斯顿罗森多夫研究中心（HZDR））；Institute of Advanced Research in Artificial Intelligence (IARAI)（人工智能高级研究院（IARAI））
Tongji University（同济大学）；Continental AG（大陆集团）；University of Bristol（布里斯托大学）
Road damage is an inconvenience and a safety hazard, severely affecting vehicle condition, driving comfort, and traffic safety. The traditional manual visual road inspection process is pricey, dangerous, exhausting, and cumbersome. Also, manual road inspection results are qualitative and subjective, as they depend entirely on the inspector's personal experience. Therefore, there is an ever-increasing need for automated road inspection systems. This chapter first compares the five most common road damage types. Then, 2-D/3-D road imaging systems are discussed. Finally, state-of-the-art machine …
Maintaining aging infrastructure is a challenge currently faced by local and national administrators all around the world. An important prerequisite for efficient infrastructure maintenance is to continuously monitor (i.e., quantify the level of safety and reliability) the state of very large structures. Meanwhile, computer vision has made impressive strides in recent years, mainly due to successful applications of deep learning models. These novel progresses are allowing the automation of vision tasks, which were previously impossible to automate, offering promising possibilities to assist ad…
The road is vital for many aspects of life, and road maintenance is crucial for human safety. One of the critical tasks to allow timely repair of road damages is to quickly and efficiently detect and classify them. This work details the strategies and experiments evaluated for these tasks. Specifically, we evaluate Detectron2's implementation of Faster R-CNN using different base models and configurations. We also experiment with these approaches using the Global Road Damage Detection Challenge 2020, A Track in the IEEE Big Data 2020 Big Data Cup Challenge dataset. The results show that the X10…
Sam Houston State University（萨姆休斯顿州立大学）；Academy of Finance（金融学院）；FPT University（FPT大学）
Maintaining roadway infrastructure is essential for ensuring a safe, efficient, and sustainable transportation system. However, manual data collection for detecting road damage is time-consuming, labor-intensive, and poses safety risks. Recent advancements in artificial intelligence, particularly deep learning, offer a promising solution for automating this process using road images. This paper presents a comprehensive workflow for road damage detection using deep learning models, focusing on optimizations for inference speed while preserving detection accuracy. Specifically, to accommodate ha…
Universiti Teknologi PETRONAS（马来西亚石油科技大学）；Université Paris-Est Créteil (UPEC)（巴黎东克雷泰伊大学）；COMSATS University Islamabad, Lahore Campus（伊斯兰堡COMSATS大学拉合尔校区）
Buses and heavy vehicles have more blind spots compared to cars and other road vehicles due to their large sizes. Therefore, accidents caused by these heavy vehicles are more fatal and result in severe injuries to other road users. These possible blind-spot collisions can be identified early using vision-based object detection approaches. Yet, the existing state-of-the-art vision-based object detection models rely heavily on a single feature descriptor for making decisions. In this research, the design of two convolutional neural networks (CNNs) based on high-level feature descriptors and thei…
Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）；University of Tokyo（东京大学）；Amazon EU（亚马逊欧盟）
Many municipalities and road authorities seek to implement automated evaluation of road damage. However, they often lack technology, know-how, and funds to afford state-of-the-art equipment for data collection and analysis of road damages. Although some countries, like Japan, have developed less expensive and readily available Smartphone-based methods for automatic road condition monitoring, other countries still struggle to find efficient solutions. This work makes the following contributions in this context. Firstly, it assesses the usability of the Japanese model for other countries. Second…
The Hong Kong University of Science and Technology（香港科技大学）
This paper presents a novel road damage detection algorithm based on unsupervised disparity map segmentation. Firstly, a disparity map is transformed by minimizing an energy function with respect to stereo rig roll angle and road disparity projection model. Instead of solving this energy minimization problem using non-linear optimization techniques, we directly find its numerical solution. The transformed disparity map is then segmented using Otus's thresholding method, and the damaged road areas can be extracted. The proposed algorithm requires no parameters when detecting road damage. The ex…
Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）；The University of Tokyo（东京大学）；UrbanX Technologies, Inc.（UrbanX科技公司）
The data article describes the Road Damage Dataset, RDD2022, which comprises 47,420 road images from six countries, Japan, India, the Czech Republic, Norway, the United States, and China. The images have been annotated with more than 55,000 instances of road damage. Four types of road damage, namely longitudinal cracks, transverse cracks, alligator cracks, and potholes, are captured in the dataset. The annotated dataset is envisioned for developing deep learning-based methods to detect and classify road damage automatically. The dataset has been released as a part of the Crowd sensing-based Ro…
Qatar Computing Research Institute（卡塔尔计算研究所）；Hamad bin Khalifa University（哈马德·本·哈利法大学）
We propose Nazr-CNN1, a deep learning pipeline for object detection and fine-grained classification in images acquired from Unmanned Aerial Vehicles (UAVs) for damage assessment and monitoring. Nazr-CNN consists of two components. The function of the first component is to localize objects (e.g. houses or infrastructure) in an image by carrying out a pixel-level classification. In the second component, a hidden layer of a Convolutional Neural Network (CNN) is used to encode Fisher Vectors (FV) of the segments generated from the first component in order to help discriminate between different lev…
National Cheng Kung University（国立成功大学）；Academia Sinica & MOST Joint Research Center for AI Technology and All Vista Healthcare（中央研究院与科技部人工智能技术暨全幅健康照护联合研究中心）
It is hard to detect on-road objects under various lighting conditions. To improve the quality of the classifier, three techniques are used. We define subclasses to separate daytime and nighttime samples. Then we skip similar samples in the training set to prevent overfitting. With the help of the outside training samples, the detection accuracy is also improved. To detect objects in an edge device, Nvidia Jetson TX2 platform, we exert the lightweight model ResNet-18 FPN as the backbone feature extractor. The FPN (Feature Pyramid Network) generates good features for detecting objects over vari…
Semantic segmentation of road scenes is one of the key technologies for realizing autonomous driving scene perception, and the effectiveness of deep Convolutional Neural Networks(CNNs) for this task has been demonstrated. State-of-art CNNs for semantic segmentation suffer from excessive computations as well as large-scale training data requirement. Inspired by the ideas of Fine-tuning-based Transfer Learning (FTT) and feature-based knowledge distillation, we propose a new knowledge distillation method for cross-domain knowledge transference and efficient data-insufficient network training, nam…
MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；Qatar Computing Research Institute, HBKU（卡塔尔计算研究所，哈马德·本·哈利法大学）
Mapping road networks is currently both expensive and labor-intensive. High-resolution aerial imagery provides a promising avenue to automatically infer a road network. Prior work uses convolutional neural networks (CNNs) to detect which pixels belong to a road (segmentation), and then uses complex post-processing heuristics to infer graph connectivity. We show that these segmentation methods have high error rates because noisy CNN outputs are difficult to correct. We propose RoadTracer, a new method to automatically construct accurate road network maps from aerial images. RoadTracer uses an i…
Among several road hazards that are present in any paved way in the world, potholes are one of the most annoying and also involving higher maintenance costs. There exists an increasing interest on the automated detection of these hazards enabled by technological and research progress. Our research work tackled the challenge of pothole detection from images of real world road scenes. The main novelty resides on the application of the latest progress in AI to learn the visual appearance of potholes. We built a large dataset of images with pothole annotations. They contained road scenes from diff…
Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）；University of Tokyo（东京大学）
This paper summarizes the Global Road Damage Detection Challenge (GRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data'2020. The Big Data Cup challenges involve a released dataset and a well-defined problem with clear evaluation metrics. The challenges run on a data competition platform that maintains a leaderboard for the participants. In the presented case, the data constitute 26336 road images collected from India, Japan, and the Czech Republic to propose methods for automatically detecting road damages in these countries. In total, 121 teams from seve…
Center for Research in Mathematics CIMAT AC（数学研究中心 CIMAT AC）；Cátedras CONACYT（CONACYT 教席）
In this work, orientation detection using Deep Learning is acknowledged for a particularly vulnerable class of road users,the cyclists. Knowing the cyclists' orientation is of great relevance since it provides a good notion about their future trajectory, which is crucial to avoid accidents in the context of intelligent transportation systems. Using Transfer Learning with pre-trained models and TensorFlow, we present a performance comparison between the main algorithms reported in the literature for object detection,such as SSD, Faster R-CNN and R-FCN along with MobilenetV2, InceptionV2, ResNet…
