5/25/2023 0 Comments Raindrop templateHalimeh and Roser developed a geometric-photometric model of raindrops on a windshield, coined Raindrop Intelligent Geometric Scanner and Environment Constructor (RIGSEC). Roser and Moosmann’s system does not detect individual raindrops on vehicle windshield which makes it less appropriate for ADAS and autonomous driving applications. Some background objects were also mistakenly detected as raindrops, which skewed the results of the classifier. In addition, for adjacent classes (e.g., “clear” and “light rain” or “Heavy rain” and “light rain”), some misclassification errors were reported. Results showed a good rate of classification for the highway-only subset (2% error) but the classification rate got worse as the complexity of the driving environment increased. To test the classifier, three subsets of images were created, each represented one driving scenario (Highway, Highway + Rural, Rural) with different weather conditions equally represented in each subset. To train the system, images were captured under different driving scenarios and labeled based on the weather condition they represented. An SVM is added to reduce dimensionality, as shown in Figure 4. The descriptor in this classifier is of size 13 (ROI areas) × 5 (features) × 10 (bins per histogram) = 650 elements. Features are measured in each ROI and the output is normalized to a value between 0 and 1, then assigned to one of 10 bins in the feature histogram. It is then divided into 12 sun regions, for a total of 13 ROI per image. The image is defined as the first global Region Of Interest (ROI). They selected a bag of features (BoF) that includes brightness, contrast, sharpness, saturation, and hue. The output of the system was a classification of the weather as “Clear”, “Light rain” or “Heavy rain”. Roser and Moosmann proposed a weather classification system based on feature histogram and Support vector machine (SVM), using a general-purpose vehicle camera. The focus of that survey was on the different approaches to the detection and removal of falling rain streaks. Tripathi and Mukhopadhyay, published a short survey paper on the falling raindrop and rain streak detection and removal algorithms. More sophisticated models for rendering falling raindrops were later developed (for example, Rousseau and Jolivet ) but Garg and Nayar’s model remained the most referenced and used in a good body of research work dealing with falling raindrops and rain streaks detection and removal. Garg and Nayar described a detailed model of falling raindrops, both dynamically (speed, size, shape) and optically (reflection, refraction, warping). A 15% to 30% increase in the number of feature points tracked successfully was observed in the de-rained image sequence vs. used the technique of motion estimation using point trajectories to evaluate the quality of their rain removal algorithm. observed that the number of feature points extracted by Harris and SURF detectors was reduced by 48% and 68%, respectively due to falling rain. In image stitching applications, Chia et al. In applications that use LIDAR and radar, rain attenuates the strength of the transmitted signals and introduces noise. Rain is a common adverse weather condition and is the focus of this survey paper. This survey paper describes the main techniques for detecting and removing adherent raindrops from images that accumulate on the protective cover of cameras.įog, as an example of adverse weather conditions, reduces the visible range of onboard cameras and causes loss of contrast and fidelity in captured images. 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. 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. Rain is a common and significant source of image quality degradation. 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. Research on the effect of adverse weather conditions on the performance of vision-based algorithms for automotive tasks has had significant interest.
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