Road rutting is a severe road distress that can cause premature failure of the 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 the 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 949 images and provides both object-level and pixel-level annotations. Object detection models and semantic segmentation models were deployed to detect road rutting on the proposed dataset, and quantitative and qualitative analysis of model predictions were done to evaluate model performance and identify challenges faced in the detection of road rutting using the proposed method. Object detection model YOLOXs achieves mAP@IoU=0.5 of 61.6% and semantic segmentation model PSPNet (Resnet-50) achieves IoU of 54.69 and accuracy of 72.67, thus providing a benchmark accuracy for similar work in future. The proposed road rutting dataset and the results of our research study will help accelerate the research on the detection of road rutting using deep learning.

Vehicle class and orientation detection is fundamental to different tasks of autonomous driving such as traffic flow estimation, vehicle re-identification, etc. Recent researches using Convolutional Neural Networks (CNNs) or Vision-based Transformers for object detection tasks require a large number of images with annotations. The collection of a large amount of high-quality dataset is expensive and time-consuming. Synthetic images obtained from driving simulators allow the generation of a large synthetic dataset under varying conditions. This paper proposes YOLOv7-based neural networks for vehicle class and orientation detection that train on the synthetic dataset made available as part of the Vehicle class and Orientation Detection Challenge 2022 of IEEE BigData 2022. Our proposed approaches included the use of data resampling methods to address data imbalance in the dataset and ensemble learning to optimize the model performance. Trained models were thoroughly evaluated on real-world test datasets. Our proposed approach achieved weighted mean average precision of 0.497 allowing us to win the challenge.

Automatic monitoring of road conditions to detect different road damage types, such as cracks, potholes, etc., at low cost is important for early detection and timely maintenance of roads. This enables road maintenance bodies to perform frequent monitoring trips given the limited availability of allocated funds and manpower. In this direction, road damage detection using image processing techniques and deep learning on wide-view road images captured from smartphones or vehicle-mounted cameras has been actively researched in the past few years. This paper proposes deep learning-based neural networks and explores the feasibility of combining road damage data from different countries for road damage detection. The models were trained on the dataset made available as part of the Crowdsensing-based Road Damage Detection Challenge (CRDDC2022) of IEEE BigData 2022. The trained models were thoroughly evaluated on test datasets. Our proposed approach achieved an average F1 score of 0.628 allowing us to be in the Top 10 of the challenge. F1 scores achieved for Overall, India, Japan, Norway, and the United States were 0.697, 0.493, 0.715, 0.461, and 0.775 respectively.