Let The Sunshine in: Sun Glare Detection on Automotive Surround-view Cameras

Kim, Minsub; Hong, Soonyoung; Kang, Moon Gi

doi:10.2352/issn.2470-1173.2020.16.avm-080

Cited by 19 publications

(13 citation statements)

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“…It commonly occurs in highway scenes due to large open areas of the sky being visible in the image containing unstructured, blurry patterns that seem very similar to soiling patterns. Sun glare [31] is another issue that makes some areas overexposed in the image leading to artifacts. These artifacts are often misclassified as opaque soiling.…”

Section: Resultsmentioning

confidence: 99%

TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric

Das¹,

Křížek²,

Sistu³

et al. 2020

Preprint

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Automotive cameras, particularly surround-view cameras, tend to get soiled by mud, water, snow, etc. For higher levels of autonomous driving, it is necessary to have a soiling detection algorithm which will trigger an automatic cleaning system. Localized detection of soiling in an image is necessary to control the cleaning system. It is also necessary to enable partial functionality in unsoiled areas while reducing confidence in soiled areas. Although this can be solved using a semantic segmentation task, we explore a more efficient solution targeting deployment in low power embedded system. We propose a novel method to regress the area of each soiling type within a tile directly. We refer to this as coverage. The proposed approach is better than learning the dominant class in a tile as multiple soiling types occur within a tile commonly. It also has the advantage of dealing with coarse polygon annotation, which will cause the segmentation task. The proposed soiling coverage decoder is an order of magnitude faster than an equivalent segmentation decoder. We also integrated it into an object detection and semantic segmentation multi-task model using an asynchronous back-propagation algorithm. A portion of the dataset used will be released publicly as part of our WoodScape dataset [1] to encourage further research.

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Section: Resultsmentioning

confidence: 99%

TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric

Das¹,

Křížek²,

Sistu³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Yahiaoui et al [193] developed their own sunshine glare dataset in autonomous driving called Woodscape, including situations like direct sunlight in the sky or sun glares on dry roads, road marks being wiped off by sun glares on wet roads, sun glares on reflective surfaces, etc. The glare is detected by an image processing algorithm with several processing blocks including color conversion, adaptive thresholding, geometric filters, and blob detection, and trained with CNN network.…”

Section: Light Relatedmentioning

confidence: 99%

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Zhang¹,

Carballo²,

Yang³

et al. 2021

Preprint

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Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences. However, autonomous driving under adverse weather conditions has been the problem that keeps autonomous vehicles (AVs) from going to level 4 or higher autonomy for a long time. This paper assesses the influences and challenges that weather brings to ADS sensors in an analytic and statistical way, and surveys the solutions against inclement weather conditions. Stateof-the-art techniques on perception enhancement with regard to each kind of weather are thoroughly reported. External auxiliary solutions like V2X technology, weather conditions coverage in currently available datasets, simulators, and experimental facilities with weather chambers are distinctly sorted out. By pointing out all kinds of major weather problems the autonomous driving field is currently facing, and reviewing both hardware and computer science solutions in recent years, this survey contributes a holistic overview on the obstacles and directions of ADS development in terms of adverse weather driving conditions.

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“…Choi et al [30] measured the density of haze features such as contrast energy and image entropy. In our previous study [31], we measured the degree of haze contained in a region using the standard deviation and estimated the APSF of the region proportional to it. However, contrast alone cannot identify whether the area in which the kernel is estimated is an area with information not visible owing to the haze or a flat area without haze.…”

Section: A Apsf Estimation Using Superpixel Algorithmmentioning

confidence: 99%

Single Image Dehazing of Road Scenes Using Spatially Adaptive Atmospheric Point Spread Function

et al. 2021

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Image haze removal is essential in autonomous driving as the outdoor images captured during unfavorable weather conditions, such as haze or snow, are affected by poor visibility. Much research has been done to overcome image degradation such as low contrast and faded color due to haze. However, in the traditional model, a phenomenon is neglected that several particles simultaneously involved in light acquisition. To address this problem, we propose a novel single image dehazing method based on the spatially adaptive atmospheric point spread function (APSF). We developed a module that estimates the APSF to overcome the limitations of the spatially invariant APSF which used in existing dehazing algorithms. The key factor in the estimation is that road scenes with haze have different statistical characteristic from common hazy images in color and resolution. Furthermore, the APSF on the traffic signs or lights is estimated by generating superpixels to prevent halo artifacts around the sharp edges of the images. We adopted the total variation model as a regularization functional to reduce halo and unnatural artifacts that may occur during deconvolution. The haze-free images from the proposed method tested whether the proposed method can enhance the performance of vision algorithms for autonomous driving. The experimental results demonstrate that the proposed method outperforms state-of-the-art image dehazing methods enhancing the performance of the vision algorithms. Moreover, additional experiments demonstrated the effectiveness of the proposed method for quantitative and qualitative comparison with the state-of-the-art algorithms.

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Let The Sunshine in: Sun Glare Detection on Automotive Surround-view Cameras

Abstract: Fast track article for IS&T International Symposium on Electronic Imaging 2020: Autonomous Vehicles and Machines proceedings.

Cited by 19 publications

References 0 publications

TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric

TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Single Image Dehazing of Road Scenes Using Spatially Adaptive Atmospheric Point Spread Function

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