Two-Class Weather Classification

Lu, Cewu; Lin, Di; Jia, Jiaya; Tang, Chi-Keung

doi:10.1109/tpami.2016.2640295

Cited by 57 publications

(38 citation statements)

References 56 publications

(33 reference statements)

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“…For instance, a CNN model coupled with sparse decomposition was trained to classify weather conditions [25]. Additionally, a binary CNN model was trained to classify images as either cloudy or sunny [26,27]. However, this model remains limited to the given binary classes of weather, ignoring the complexity of the addressed subject.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

“…Similarly, the Multi-class Weather Image (MWI) dataset consists of 20,000 images of different weather conditions [19]. Another example is a binary weather dataset that contains 10,000 images belonging to either sunny or cloudy weather [27]. Additionally, a large dataset of images is presented to describe weather conditions from the aspect of cloud intensity such as clear, partly cloudy, mostly cloudy, or cloudy including the time and location data [40].…”

Section: Datamentioning

confidence: 99%

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

Ibrahim

Haworth

Cheng

2019

IJGI

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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. environment due to precipitation including clear, rainy, foggy, or snowy weather. They represent crucial factors for many urban studies including transport, behaviour, and safety-related research [5]. For example, walking, cycling, or driving in rainy weather is associated with a higher risk of experiencing an incident than in clear weather [5,6]. Fog, snow, and glare have also been found to increase risk [6,7]. Importantly, it is not only the inherent risk that different weather and visual conditions pose to human life that is of interest to researchers. Scene awareness for autonomous navigation in cities is highly influenced by the dynamics of weather and visual conditions and it is imperative for any vision system to cope with them simultaneously [8]. For example, object detection algorithms must perform well in fog and glare as well as in clear conditions, in order to be reliable. Accordingly, finding an automatic approach to extract this information from images or video streams is in high demand for computer scientists, planners, and policy-makers.While there are different methods that are used to understand the dynamics of weather and visual conditions, a knowledge gap appears when addressing this subject. To date, these two crucial domains-weather and visual conditions-have been studied individually, ignoring the importance of understanding the dy...

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Section: Deep Learning Modelsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

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

Ibrahim

Haworth

Cheng

2019

IJGI

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“…It is well known that a practical autonomous driving system requires reliable and effective traffic scene understanding [1–4]. Among them, the recognition of special traffic scenes is vital to the perception model.…”

Section: Introductionmentioning

confidence: 99%

“…In the latter case, rule‐based traffic sign detection is not reliable and may cause risks since the model‐based feature extraction process discards a large amount of scene information and is difficult to cope with uncertain scenes. Some others focus on natural traffic scene recognition [1–4, 8, 9] such as weather type and the time of occurrence. Most of them aim to develop a more robust model with complex scene changes for low‐level perception tasks, such as dynamic vehicle detection under different weather/illumination conditions.…”

Section: Introductionmentioning

confidence: 99%

Scene recognition under special traffic conditions based on deep multi‐task learning

Xiao

et al. 2020

J. eng.

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“…[19][20][21][22][23][24] This is because rain, snow, and fog weather events, smoke, haze, or other changes in lighting and visibility can significantly obscure features, degrade object recognition, and modify the saliency and image context of an outdoor scene. [25][26][27][28][29][30][31][32] Naturally, scene-depicted environmental conditions can vary with time of day, season, and location. 33 Similar challenges can also extend to interpreting space-and time-changing scenes due to visual motion of objects within the field of view.…”

Section: Introductionmentioning

confidence: 99%

SpaceTime Environmental Image Information for Scene Understanding

Tunick¹

2016

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Approved for public release; distribuiton unlimited. ii REPORT DOCUMENTATION PAGEForm Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY) April 20162. REPORT TYPE ARL-TR-7652 SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S) SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution unlimited. SUPPLEMENTARY NOTES ABSTRACTThe interpretation of space-and time-varying environmental image information can pose serious challenges for rapid and robust scene understanding, particularly for problems of interest to the Army. Changing environmental conditions, such as illumination, precipitation, and vegetation, can obscure features, degrade object recognition, and modify saliency and image context. Current methods for scene understanding do not address elements of image information (and image context) affected by space-and time-changing environmental conditions, and as a result, important and meaningful features of the image data may be overlooked. In this report, we propose that it is important to incorporate space-and time-varying environmental image information from the very beginning of the data collection process so that the recorded images can be more effectively indexed and retrieved for operational use and analysis. This top-down approach not only provides a systematic characterization of the measured data for better scene description, but can also help the end user (Soldier) develop improved course of action strategies based on scene understanding (algorithms and analysis) incorporating battlefield environments changing in space and time.

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Two-Class Weather Classification

Cited by 57 publications

References 56 publications

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

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

Scene recognition under special traffic conditions based on deep multi‐task learning

SpaceTime Environmental Image Information for Scene Understanding

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