Challenges in Object Detection Under Rainy Weather Conditions

Hasirlioglu, Sinan; Riener, Andreas

doi:10.1007/978-3-030-14757-0_5

Cited by 18 publications

(12 citation statements)

References 9 publications

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“…In this article, the visibility and background attenuation of the object has been taken into account. Weather can significantly degrade detection for some sensors [ 55 , 56 ]. Three different effects can occur: reduction of visibility, particles appearing in the image, and, in the case of optical sensors, particles on the detector can obscure or blur regions.…”

Section: Modelingmentioning

confidence: 99%

Placement of Optical Sensors in 3D Terrain Using a Bacterial Evolutionary Algorithm

Kovács

Bolemányi

Botzheim

2022

Sensors

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This paper proposes an optimization framework for terrain large scale optical sensor placement to improve border protection. Compared to the often used, maximal coverage of an area approach, this method minimizes the undetected passages in the monitored area. Border protection is one of the most critical areas for sensor placement. Unlike traditional border protection solutions, we do not optimize for 2D but for 3D to prevent transit. Additionally, we consider both natural and built environmental coverings. The applied environmental model creates a highly inhomogeneous sensing area for sensors instead of the previously used homogeneous one. The detection of each sensor was provided by a line-of-sight model supplemented with inhomogeneous probabilities. The optimization was performed using a bacterial evolutionary algorithm. In addition to maximizing detection, minimizing the number of the applied sensors played a crucial role in design. These two cost components are built on each other hierarchically. The developed simulation framework based on ray tracing provided an excellent opportunity to optimize large areas. The presented simulation results prove the efficiency of this method. The results were evaluated by testing on a large number of intruders. Using sensors with different quantities and layouts in the tested 1×1×1 km environment, we reduced the probability of undetected intrusion to below 0.1% and increased the probability of acceptable classification to 99%.

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Section: Modelingmentioning

confidence: 99%

Placement of Optical Sensors in 3D Terrain Using a Bacterial Evolutionary Algorithm

Kovács

Bolemányi

Botzheim

2022

Sensors

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“…In [ 90 ], a mathematical model is presented to test wet snowfall output; the analysis shows that radar output has a similar effect on snowfall to that of rainfall. Although radar performance in precipitate surroundings has been observed to degrade, the study in [ 91 , 92 ], compares radar, lidar, and camera performances in simulated and real-world adverse climatic environments and concludes that radar outperforms lidar and cameras under the influence of rain. In [ 93 , 94 ], the effect of aerosols upon the transmission of radar signals has been investigated in controlled environments and mining applications, and the results show that radar is not impacted by the presence of airborne particles, such as dust and smoke because of its wavelengths, which are much larger than the characteristic dimensions of dust.…”

Section: Sensorsmentioning

confidence: 99%

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Mohammed

Amamou

Ayevide

et al. 2020

Sensors

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Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system.

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“…To characterize sensor distortions, previous methods have been focused on evaluating automotive sensors in challenging situations such as dust, smoke, rain, fog and snow [40]. For these evaluations, testing facilities such as Cerema [8] and Carissma [18] provide reproducible adverse weather situations with defined and adjustable severity. In particular, cameras suffer from reduced contrast because particles in the air cause scattering [4,36].…”

Section: Depth Datasetsmentioning

confidence: 99%

Pixel-Accurate Depth Evaluation in Realistic Driving Scenarios

Gruber

Bijelic

Heide

et al. 2019

2019 International Conference on 3D Vision (3DV)

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This work introduces an evaluation benchmark for depth estimation and completion using high-resolution depth measurements with angular resolution of up to 25" (arcsecond), akin to a 50 megapixel camera with per-pixel depth available. Existing datasets, such as the KITTI benchmark [13], provide only sparse reference measurements with an order of magnitude lower angular resolution -these sparse measurements are treated as ground truth by existing depth estimation methods. We propose an evaluation methodology in four characteristic automotive scenarios recorded in varying weather conditions (day, night, fog, rain). As a result, our benchmark allows us to evaluate the robustness of depth sensing methods in adverse weather and different driving conditions. Using the proposed evaluation data, we demonstrate that current stereo approaches provide significantly more stable depth estimates than monocular methods and lidar completion in adverse weather. Data and code are available at https://github.com/ gruberto/PixelAccurateDepthBenchmark.git.

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Challenges in Object Detection Under Rainy Weather Conditions

Cited by 18 publications

References 9 publications

Placement of Optical Sensors in 3D Terrain Using a Bacterial Evolutionary Algorithm

Placement of Optical Sensors in 3D Terrain Using a Bacterial Evolutionary Algorithm

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Pixel-Accurate Depth Evaluation in Realistic Driving Scenarios

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