scholarly journals WeatherNet: Recognising Weather and Visual Conditions from Street-Level Images Using Deep Residual Learning

2019 ◽  
Vol 8 (12) ◽  
pp. 549 ◽  
Author(s):  
Mohamed Ibrahim ◽  
James Haworth ◽  
Tao Cheng
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Wide Spectrum ◽  
Weather Conditions ◽  
Machine Intelligence ◽  
Residual Learning ◽  
Street Level ◽  
Unified Method ◽  
Visual Conditions

Extracting information related to weather and visual conditions at a given time and space is indispensable for scene awareness, which strongly impacts our behaviours, from simply walking in a city to riding a bike, driving a car, or autonomous drive-assistance. Despite the significance of this subject, it has still not been fully addressed by the machine intelligence relying on deep learning and computer vision to detect the multi-labels of weather and visual conditions with a unified method that can be easily used in practice. What has been achieved to-date are rather sectorial models that address a limited number of labels that do not cover the wide spectrum of weather and visual conditions. Nonetheless, weather and visual conditions are often addressed individually. In this paper, we introduce a novel framework to automatically extract this information from street-level images relying on deep learning and computer vision using a unified method without any pre-defined constraints in the processed images. A pipeline of four deep convolutional neural network (CNN) models, so-called WeatherNet, is trained, relying on residual learning using ResNet50 architecture, to extract various weather and visual conditions such as dawn/dusk, day and night for time detection, glare for lighting conditions, and clear, rainy, snowy, and foggy for weather conditions. WeatherNet shows strong performance in extracting this information from user-defined images or video streams that can be used but are not limited to autonomous vehicles and drive-assistance systems, tracking behaviours, safety-related research, or even for better understanding cities through images for policy-makers.

scholarly journals Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7267
Author(s):  
Luiz G. Galvao ◽  
Maysam Abbod ◽  
Tatiana Kalganova ◽  
Vasile Palade ◽  
Md Nazmul Huda
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Vehicle Detection ◽  
Weather Conditions ◽  
Detection Performance ◽  
The Past ◽  
Perception System ◽  
Good Detection

Autonomous Vehicles (AVs) have the potential to solve many traffic problems, such as accidents, congestion and pollution. However, there are still challenges to overcome, for instance, AVs need to accurately perceive their environment to safely navigate in busy urban scenarios. The aim of this paper is to review recent articles on computer vision techniques that can be used to build an AV perception system. AV perception systems need to accurately detect non-static objects and predict their behaviour, as well as to detect static objects and recognise the information they are providing. This paper, in particular, focuses on the computer vision techniques used to detect pedestrians and vehicles. There have been many papers and reviews on pedestrians and vehicles detection so far. However, most of the past papers only reviewed pedestrian or vehicle detection separately. This review aims to present an overview of the AV systems in general, and then review and investigate several detection computer vision techniques for pedestrians and vehicles. The review concludes that both traditional and Deep Learning (DL) techniques have been used for pedestrian and vehicle detection; however, DL techniques have shown the best results. Although good detection results have been achieved for pedestrians and vehicles, the current algorithms still struggle to detect small, occluded, and truncated objects. In addition, there is limited research on how to improve detection performance in difficult light and weather conditions. Most of the algorithms have been tested on well-recognised datasets such as Caltech and KITTI; however, these datasets have their own limitations. Therefore, this paper recommends that future works should be implemented on more new challenging datasets, such as PIE and BDD100K.

scholarly journals Computer vision based obstacle detection and target tracking for autonomous vehicles

2021 ◽  
Vol 336 ◽  
pp. 07004
Author(s):  
Ruoyu Fang ◽  
Cheng Cai
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Target Tracking ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Pid Algorithm ◽  
Deep Learning Neural Network

Obstacle detection and target tracking are two major issues for intelligent autonomous vehicles. This paper proposes a new scheme to achieve target tracking and real-time obstacle detection of obstacles based on computer vision. ResNet-18 deep learning neural network is utilized for obstacle detection and Yolo-v3 deep learning neural network is employed for real-time target tracking. These two trained models can be deployed on an autonomous vehicle equipped with an NVIDIA Jetson Nano motherboard. The autonomous vehicle moves to avoid obstacles and follow tracked targets by camera. Adjusting the steering and movement of the autonomous vehicle according to the PID algorithm during the movement, therefore, will help the proposed vehicle achieve stable and precise tracking.

scholarly journals Lightweight Object Detection Ensemble Framework for Autonomous Vehicles in Challenging Weather Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Rahee Walambe ◽  
Aboli Marathe ◽  
Ketan Kotecha ◽  
George Ghinea
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Autonomous Vehicles ◽  
Data Augmentation ◽  
Weather Conditions ◽  
Training Data ◽  
Average Precision ◽  
Ensemble Techniques ◽  
Rainy Weather ◽  
Increase In Accuracy

The computer vision systems driving autonomous vehicles are judged by their ability to detect objects and obstacles in the vicinity of the vehicle in diverse environments. Enhancing this ability of a self-driving car to distinguish between the elements of its environment under adverse conditions is an important challenge in computer vision. For example, poor weather conditions like fog and rain lead to image corruption which can cause a drastic drop in object detection (OD) performance. The primary navigation of autonomous vehicles depends on the effectiveness of the image processing techniques applied to the data collected from various visual sensors. Therefore, it is essential to develop the capability to detect objects like vehicles and pedestrians under challenging conditions such as like unpleasant weather. Ensembling multiple baseline deep learning models under different voting strategies for object detection and utilizing data augmentation to boost the models’ performance is proposed to solve this problem. The data augmentation technique is particularly useful and works with limited training data for OD applications. Furthermore, using the baseline models significantly speeds up the OD process as compared to the custom models due to transfer learning. Therefore, the ensembling approach can be highly effective in resource-constrained devices deployed for autonomous vehicles in uncertain weather conditions. The applied techniques demonstrated an increase in accuracy over the baseline models and were able to identify objects from the images captured in the adverse foggy and rainy weather conditions. The applied techniques demonstrated an increase in accuracy over the baseline models and reached 32.75% mean average precision (mAP) and 52.56% average precision (AP) in detecting cars in the adverse fog and rain weather conditions present in the dataset. The effectiveness of multiple voting strategies for bounding box predictions on the dataset is also demonstrated. These strategies help increase the explainability of object detection in autonomous systems and improve the performance of the ensemble techniques over the baseline models.

scholarly journals Deep Learning for COVID-19 Recognition

2021 ◽  
Author(s):  
Yuchen Zhang ◽  
Yanyan Zhang
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Cause Of Death ◽  
X Ray ◽  
Learning Framework ◽  
Residual Learning ◽  
Epidemic Period ◽  
Chest X Ray ◽  
New Type

Pneumonia is a leading cause of death worldwide, and one of the most significant approaches to diagnose pneumonia is Chest X-ray (CXR) since it was used in clinical scenes. Convolutional neural networks (CNNs) have been widely used in computer vision community. Along with the development of CNNs, we want to make use of CNNs to recognize CXR of people who get pneumonia and make classification. It is important, especially during epidemic period. In this paper, we present a new type of residual learning framework, PEPX-Resnet, which makes use of a type of lightweight residual, and apply this network to CXR dataset. The result shows that PEPX-Resnet is easier to optimize and can have better results, especially for COVID-19 cases. PEPX-Resnet could reach higher accuracy, f1 score and some other evaluations for CXR dataset.

scholarly journals Applicability of Computer Vision Architectures and Their Influence on Traffic Safety of Autonomous Vehicles

2019 ◽  
Vol 8 (6) ◽  
pp. 5295-5301 ◽  
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Vision System ◽  
Autonomous Vehicle ◽  
Computing Time ◽  
Weather Conditions ◽  
Network Architectures ◽  
Neural Network Architectures

This article considers modern rapid architectures of detecting neural networks, structural peculiarities of each selected neural network architectures are analyzed. Experiment is carried out on the basis of potentially dangerous situation upon autonomous vehicle movement; in the selected experimental environment a set of architectures for computer vision system of autonomous vehicle is analyzed, and traffic safety of autonomous vehicle is estimated under various weather conditions; computing time required for application of additional control and analysis algorithms is evaluated. Experimental results are analyzed aiming at development of reasonable selection of neural network architectures for object recognition required for variability of support of autonomous vehicle traffic. Conclusion about applicability of the considered neural network architectures is made for conditions of certain project.

scholarly journals Tensor Methods in Computer Vision and Deep Learning

2021 ◽  
Vol 109 (5) ◽  
pp. 863-890
Author(s):  
Yannis Panagakis ◽  
Jean Kossaifi ◽  
Grigorios G. Chrysos ◽  
James Oldfield ◽  
Mihalis A. Nicolaou ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Tensor Methods

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

scholarly journals A deep learning approach to identify unhealthy advertisements in street view images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gregory Palmer ◽  
Mark Green ◽  
Emma Boyland ◽  
Yales Stefano Rios Vasconcelos ◽  
Rahul Savani ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fast Food ◽  
Social Inequalities ◽  
Social Inequalities In Health ◽  
Street Level ◽  
Street View ◽  
Food Advertisements ◽  
Data Collection Effort ◽  
Deprived Areas

AbstractWhile outdoor advertisements are common features within towns and cities, they may reinforce social inequalities in health. Vulnerable populations in deprived areas may have greater exposure to fast food, gambling and alcohol advertisements, which may encourage their consumption. Understanding who is exposed and evaluating potential policy restrictions requires a substantial manual data collection effort. To address this problem we develop a deep learning workflow to automatically extract and classify unhealthy advertisements from street-level images. We introduce the Liverpool $${360}^{\circ }$$ 360 ∘ Street View (LIV360SV) dataset for evaluating our workflow. The dataset contains 25,349, 360 degree, street-level images collected via cycling with a GoPro Fusion camera, recorded Jan 14th–18th 2020. 10,106 advertisements were identified and classified as food (1335), alcohol (217), gambling (149) and other (8405). We find evidence of social inequalities with a larger proportion of food advertisements located within deprived areas and those frequented by students. Our project presents a novel implementation for the incidental classification of street view images for identifying unhealthy advertisements, providing a means through which to identify areas that can benefit from tougher advertisement restriction policies for tackling social inequalities.

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