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GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions
Image restoration in adverse weather conditions is a difficult task in computer vision. In this paper, we propose a novel transformer-based framework...
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Low-light DEtection TRansformer (LDETR): object detection in low-light and adverse weather conditions
Object detection has recently gained popularity mainly due to the development of deep learning techniques. However, undesirable noise challenges...
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Yolov4-based hybrid feature enhancement network with robust object detection under adverse weather conditions
Investigations into the behaviour of pedestrians and autonomous driving both frequently employ object detection. It has always been a popular area...
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RDC-YOLOv5: Improved Safety Helmet Detection in Adverse Weather
Outdoor construction sites are frequently affected by fog and various adverse weather conditions, resulting in a decline in the quality of the...
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A novel OYOLOV5 model for vehicle detection and classification in adverse weather conditions
An autonomous vehicle must accurately detect its surrounding environment to operate reliably. Adverse weather conditions (ADWC) are snow, rain, sand,...
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Object detection in adverse weather condition for autonomous vehicles
As self-driving or autonomous vehicles proliferate in our society, there is a need for their computing vision systems to be able to identify objects...
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DENet: Detection-driven Enhancement Network for Object Detection Under Adverse Weather Conditions
Recently, the deep learning-based object detection methods have achieved a great success. However, the performance of such techniques deteriorates on...
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Pothole detection in adverse weather: leveraging synthetic images and attention-based object detection methods
Potholes are a pervasive road hazard with the potential to cause accidents and vehicle damage. Detecting potholes accurately is essential for timely...
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FTDNet: Joint Semantic Learning for Scene Text Detection in Adverse Weather Conditions
In recent years, convolutional neural network (CNN)-based scene text detection methods have been extensively studied and obtained successful results...
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CCA-Based Fusion of Camera and Radar Features for Target Classification Under Adverse Weather Conditions
Deep learning models such as deep convolutional neural networks (DCNNs) image classifiers have achieved outstanding performance over the last decade....
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Novel deeper AWRDNet: adverse weather-affected night scene restorator cum detector net for accurate object detection
Object detection in adversarial atmospheric attacks, such as fog, rain, low light, and dust conditions, is a challenging task with regards to...
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Master of All: Simultaneous Generalization of Urban-Scene Segmentation to All Adverse Weather Conditions
Computer vision systems for autonomous navigation must generalize well in adverse weather and illumination conditions expected in the real world....
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Safety Monitoring for Pedestrian Detection in Adverse Conditions
Pedestrian detection is an important part of the perception system of autonomous vehicles. Foggy and low-light conditions are quite challenging for...
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Enhancing Lidar and Radar Fusion for Vehicle Detection in Adverse Weather via Cross-Modality Semantic Consistency
The fusion of multiple sensors such as Lidar and Radar provides richer information and thus improves the perception ability for autonomous driving...
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Multi-level Attention Network with Weather Suppression for All-Weather Action Detection in UAV Rescue Scenarios
Unmanned Aerial Vehicles (UAVs) possess significant advantages in terms of mobility and range compared to traditional surveillance cameras. Human...
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Internet of things based intelligent accident avoidance system for adverse weather and road conditions
Study shows that road accidents cause nearly 6,000 people to die and more than 400,000 people injured in the United States every year. Adverse...
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Performance Evaluation of Object Detection Algorithms Under Adverse Weather Conditions
Camera systems capture images from the surrounding environment and process these datastreams to detect and classify objects. However, these systems...
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Prediction of flight departure delays caused by weather conditions adopting data-driven approaches
In this study, we utilize data-driven approaches to predict flight departure delays. The growing demand for air travel is outpacing the capacity and...
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Boosting power line inspection in bad weather: Removing weather noise with channel-spatial attention-based UNet
Power line inspection based on UAVs can effectively improve the inspection efficiency. With the development of object detection algorithms, automatic...
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Real-time weather monitoring and desnowification through image purification
Object detection and tracking are essential for reliable decision-making in modern applications, such as self-driving cars, drones, and industry....
