Modelisation of Raindrops Based on Declivity Principle

Fouad, Elhassouni; Alami, Salma; Ezzine, Abdelhak

doi:10.1109/cgiv.2016.55

Cited by 2 publications

(3 citation statements)

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“…Fouad et al [ 11 ] proposed using a declivity operator to model intensity variations between the adherent raindrop and the background. As shown in Figure 5 a,b, adherent raindrop, consists of either two dark regions, separated by light one or two light regions separated by a dark one.…”

Section: Adherent Raindrop Modelsmentioning

confidence: 99%

“…This survey paper describes another form of rain, adherent raindrops on the windshield. Due to their irregular shapes, closeness to image sensors, and long temporal presence in captured image frames, adherent raindrop detection and removal problem is relatively harder than that of rain streaks detection and removal [ 11 , 12 ]. Raindrops in these two forms share some common characteristics, such as refraction properties of a raindrop, higher intensity of the drop compared to the background, and average raindrop size.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Detection and Removal of Raindrops in Automotive Vision Systems

Hamzeh

Rawashdeh

2021

J. Imaging

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Research on the effect of adverse weather conditions on the performance of vision-based algorithms for automotive tasks has had significant interest. It is generally accepted that adverse weather conditions reduce the quality of captured images and have a detrimental effect on the performance of algorithms that rely on these images. Rain is a common and significant source of image quality degradation. Adherent rain on a vehicle’s windshield in the camera’s field of view causes distortion that affects a wide range of essential automotive perception tasks, such as object recognition, traffic sign recognition, localization, mapping, and other advanced driver assist systems (ADAS) and self-driving features. As rain is a common occurrence and as these systems are safety-critical, algorithm reliability in the presence of rain and potential countermeasures must be well understood. This survey paper describes the main techniques for detecting and removing adherent raindrops from images that accumulate on the protective cover of cameras.

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Section: Adherent Raindrop Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review of Detection and Removal of Raindrops in Automotive Vision Systems

Hamzeh

Rawashdeh

2021

J. Imaging

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“…Most of the research work (see, for example, [2][3][4]) is focused on image restoration of rained images, by applying a de-raining process on them. As we have shown in our survey paper on adherent raindrop removal techniques [5], none of the reviewed de-raining algorithms could perfectly restore the rained images to resemble the clear ones.…”

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confidence: 99%

Improving the performance of automotive vision‐based applications under rainy conditions

Hamzeh

Mohammadi

Rawashdeh

2022

IET Image Processing

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Input images are the main source of information for vision-based algorithms. The presence of raindrops in input images degrades their quality and, consequently, reduces the quality of the target vision-based algorithm that consumes them. Many image restoration algorithms were proposed in the literature to remove rain presence in images to improve the input image quality. These algorithms, however, cannot remove all the raindrop presence and sometimes introduce undesirable side-effects, such as the blurring rain-occluded sections of the image and incorrectly de-raining areas in the image that are clear. It is hypothesized that a comparable performance improvement can be achieved by decreasing the sensitivity of vision-based algorithms to noisy input images, rather than denoising these images, through the process of de-raining. To test this hypothesis, the performance of state-ofthe-art object detection and semantic segmentation models was evaluated, with de-rained image datasets used as input, and compared it to that performance of the same models, retrained with rained image sets. Results showed that the performance of the retrained models was better than that of the baseline detector with de-rained images used as input. INTRODUCTIONAutomotive systems including vision-based applications are highly regulated and are required to meet high performance and safety standards. This means that these systems must operate under all conditions, favourable or adverse. The quality of the system inputs has a direct impact on its performance, in the sense that noisy inputs result in degradation in system performance.Two approaches are usually implemented to reduce the effect of noisy inputs on system performance, denoising the inputs, or reducing system sensitivity to noise. Filtering analogue signals and debouncing digital ones are two examples of common input signal denoising techniques. Predictive modelling and sensor fusion are system design techniques that lead to reduced system sensitivity to noisy inputs.Rain is a type of adverse weather condition that degrades the quality of images and the performance of vision-based algorithms that consume them. In a previous research work [1], we showed that the performance of state-of-the-art object detectors (including YOLOv3, RCNN, and SSD) greatly degradesThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Modelisation of Raindrops Based on Declivity Principle

Cited by 2 publications

References 4 publications

A Review of Detection and Removal of Raindrops in Automotive Vision Systems

A Review of Detection and Removal of Raindrops in Automotive Vision Systems

Improving the performance of automotive vision‐based applications under rainy conditions

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