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Found 9 papers, 5 papers with code
Fusion-GRU: A Deep Learning Model for Future Bounding Box Prediction of Traffic Agents in Risky Driving Videos
To ensure the safe and efficient navigation of autonomous vehicles and advanced driving assistance systems in complex traffic scenarios, predicting the future bounding boxes of surrounding traffic agents is crucial.
An Attention-guided Multistream Feature Fusion Network for Localization of Risky Objects in Driving Videos
To this end, this paper proposes an attention-guided multistream feature fusion network (AM-Net) to localize dangerous traffic agents from dashcam videos.
A Multitask Deep Learning Model for Parsing Bridge Elements and Segmenting Defect in Bridge Inspection Images
Quantitative and qualitative results from evaluating the developed multitask deep model demonstrate its advantages over the single-task-based model not only in performance (2. 59% higher mIoU on bridge parsing and 1. 65% on corrosion segmentation) but also in computational time and implementation capability.
A Deep Neural Network for Multiclass Bridge Element Parsing in Inspection Image Analysis
Aerial robots such as drones have been leveraged to perform bridge inspections.
A semi-supervised self-training method to develop assistive intelligence for segmenting multiclass bridge elements from inspection videos
This paper is motivated to develop an assistive intelligence model for segmenting multiclass bridge elements from inspection videos captured by an aerial inspection platform.
Crash Report Data Analysis for Creating Scenario-Wise, Spatio-Temporal Attention Guidance to Support Computer Vision-based Perception of Fatal Crash Risks
Then, exploratory analysis of location- and time-related variables of the crash report data suggests reducing fatal crashes to spatially defined groups.
Towards explainable artificial intelligence (XAI) for early anticipation of traffic accidents
It confirms that the Grad-CAM chosen by this study can generate high-quality, human-interpretable saliency maps (with 1. 23 Normalized Scanpath Saliency) for explaining the crash anticipation decision.
A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents
Visual cues for predicting a future accident are embedded deeply in dashcam video data.
Ranked #1 on
Accident Anticipation
on CCD
A system of vision sensor based deep neural networks for complex driving scene analysis in support of crash risk assessment and prevention
The paper further evaluates the performance of the Multi-Net and the efficiency of the developed system.
