Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classification

Classification using supervised learning requires annotating a large amount of classes-balanced data for model training and testing. This has practically limited the scope of applications with supervised learning, in particular deep learning. To address the issues associated with limited and imbalanced data, this paper introduces a sample-efficient co-supervised learning paradigm (SEC-CGAN), in which a conditional generative adversarial network (CGAN) is trained alongside the classifier and supplements semantics-conditioned, confidence-aware synthesized examples to the annotated data during the training process. In this setting, the CGAN not only serves as a co-supervisor but also provides complementary quality examples to aid the classifier training in an end-to-end fashion. Experiments demonstrate that the proposed SEC-CGAN outperforms the external classifier GAN (EC-GAN) and a baseline ResNet-18 classifier. For the comparison, all classifiers in above methods adopt the ResNet-18 architecture as the backbone. Particularly, for the Street View House Numbers dataset, using the 5% of training data, a test accuracy of 90.26% is achieved by SEC-CGAN as opposed to 88.59% by EC-GAN and 87.17% by the baseline classifier; for the highway image dataset, using the 10% of training data, a test accuracy of 98.27% is achieved by SEC-CGAN, compared to 97.84% by EC-GAN and 95.52% by the baseline classifier.Comment: 14 pages, 5 figure

Monitoring and Assessing Traffic Safety at Signalized Intersections Using Live Video Images

0013527Signalized intersections represent the most hazard spots on a roadway network. Road users are required to be alert and timely process and respond to a variety of information at signalized intersections, including traffic signal indications and changes, signage, pavement marking, road conditions, and a mix of various road users in conflict. Traditional road safety diagnosis has been conducted in a reactive manner based on crashes that had occurred. However, to effectively reduce and eventually eliminate crashes, proactive approaches are needed. Following this direction, traffic conflict events have been collected more frequently and used as a surrogate safety measure for traffic crashes. The goal of Vision Zero would only be possible if the inconsequential event data, such as traffic conflicts, can be objectively and systematically collected and effectively utilized to diagnose and improve road safety such that consequential crash events can be prevented. In this study, the art of deep learning, multiple objects detection and tracking were explored and tested in the domain of traffic conflict monitoring and assessing. As a result, an artificial intelligence (AI) enhanced computational system was developed to automate the detection and quantification of traffic conflict events as they occur in real time using traffic monitoring cameras currently installed by transportation agencies

A machine learning model pipeline for detecting wet pavement condition from live scenes of traffic cameras

Highway safety is largely influenced by weather conditions that have become increasingly volatile due to the climate change. It well known that wet pavement significantly reduces surface friction, leading to inflated collision risk. Thus, timely knowledge of the road surface condition is critical for safe driving. In this paper, a novel machine learning model pipeline is proposed to detect the wetness of pavement based on live images of highway scenes captured by publicly accessible traffic cameras. To simplify the learning task, we finetuned the state-of-the-art instance segmentation baseline models to extract background instance targets, including pavement, sky, and vegetation, which are common in highway scenes. Then, the color mixture attributes in HSV (hue, saturation and value) of each segmented instance were extracted and used as visual cues for inferring pavement condition. Finally, gradient boosting ensemble classifiers are constructed and trained using the HSV features to predict the wetness of pavement. For the segmentation task, we leveraged Detectron2 baseline models (Mask R-CNN) and evaluated three backbone networks: R50-FPN, R101-FPN, and X101-FPN. For the classification task, two most popular gradient boosting algorithms (XGBoost and CatBoost) were evaluated together with a classic logistic model. Based on experiments with our custom dataset, the best performance (F1 score: 0.927, AUC: 0.975) was achieved by the R101-FPN backbone coupled with the CatBoost classifier

Enhancing Reliability Analysis with Multisource Data: Mitigating Adverse Selection Problems in Bridge Monitoring and Management

Data collected using sensors plays an essential role in active bridge health monitoring. When analyzing a large number of bridges in the U.S., the National Bridge Inventory data as been widely used. Yet, the database does not provide information about live loads, one of the most indeterminate variables for monitoring bridges. Such asymmetric information can lead to an adverse selection problem in making maintenance, rehabilitation, and repair decisions. This study proposes a data-driven reliability analysis to assess probabilities of bridge failure by synthesizing NBI data and Weigh-In-Motion (WIM) data for a large number of bridges in Georgia. On the resistance side, tree ensemble methods are employed to support the hypothesis that the NBI operating load rating represents the distribution of bridge resistance capacities which change over time. On the loading side, the live load distribution is derived from field data collected using WIM sensors. Our results show that the proposed WIM data-enabled reliability analysis substantially enhances information symmetry and provides a reliability index that supports monitoring of bridge conditions, depending on live loads and load-carrying capacities

Deep-Learning-Based Temporal Prediction for Mitigating Dynamic Inconsistency in Vehicular Live Loads on Roads and Bridges

Weigh-In-Motion (WIM) data have been collected by state departments of transportation (DOT) in the U.S. and are anticipated to grow as state DOTs expand the number of WIM sites in order to better manage transportation infrastructure and enhance mobility. Traditional approaches for monitoring the vehicle weight measured in WIM systems include conducting statistical tests between two datasets obtained from two calibration visits. Depending on the frequency of visits, these traditional approaches are ineffective or resource-demanding for identifying calibration needs. Excessive vehicle-weight drifts exceeding 10% are usually indicative of poor performance by WIM systems. However, it has been difficult to consistently monitor such performance due to the sheer amount of data. In Georgia, the number of WIM sites have expanded from 12 to 29 in the past 3 years. This paper proposes a deep-learning-based temporal prediction approach for modeling sequential data and monitoring the time-history of the live loads imposed on roads and bridges. In total, 29 WIM sites in Georgia are analyzed to examine the effectiveness of a proposed temporal prediction approach for evaluating observed live loads. This study finds that the Jensen–Shannon divergence method is more effective than statistical difference tests, particularly when screening for live load anomalies. It is concluded that a LSTM neural network is able to capture temporal dynamics underlying the sequential load patterns observed in the WIM data and serves as an effective model for consistently monitoring the performance of WIM systems over time