Real-time rain severity detection for autonomous driving applications

Kondapalli, Chaitanya Pavan Tanay; Vaibhav, Veer; Konda, Krishna Reddy; Praveen, Kyatham; Kondoju, Bhanu

doi:10.1109/iv48863.2021.9575264

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…Te authors of [58] propose an algorithm for rain detection and severity classifcation to use in vision-based systems of autonomous driving scenarios. Te algorithm is based on a neural network that takes localised discrete cosine transform (DCT) and image-based features as input.…”

Section: Real-time Frame-based Methodologiesmentioning

confidence: 99%

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Driving in the Rain: A Survey toward Visibility Estimation through Windshields

Morden,

Caraffini,

Kypraios

et al. 2023

International Journal of Intelligent Systems

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Rain can significantly impair the driver’s sight and affect his performance when driving in wet conditions. Evaluation of driver visibility in harsh weather, such as rain, has garnered considerable research since the advent of autonomous vehicles and the emergence of intelligent transportation systems. In recent years, advances in computer vision and machine learning led to a significant number of new approaches to address this challenge. However, the literature is fragmented and should be reorganised and analysed to progress in this field. There is still no comprehensive survey article that summarises driver visibility methodologies, including classic and recent data-driven/model-driven approaches on the windshield in rainy conditions, and compares their generalisation performance fairly. Most ADAS and AD systems are based on object detection. Thus, rain visibility plays a key role in the efficiency of ADAS/AD functions used in semi- or fully autonomous driving. This study fills this gap by reviewing current state-of-the-art solutions in rain visibility estimation used to reconstruct the driver’s view for object detection-based autonomous driving. These solutions are classified as rain visibility estimation systems that work on (1) the perception components of the ADAS/AD function, (2) the control and other hardware components of the ADAS/AD function, and (3) the visualisation and other software components of the ADAS/AD function. Limitations and unsolved challenges are also highlighted for further research.

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Section: Real-time Frame-based Methodologiesmentioning

confidence: 99%

“…Te authors of [58] propose a solution for visibility based on the severity of the rain. Teir system uses images captured from a camera installed in the vehicle and analyses the rain droplets on the sensor screen as well as the overall loss of information (or entropy) caused by the rain.…”

Section: Custom Simulationmentioning

confidence: 99%

Driving in the Rain: A Survey toward Visibility Estimation through Windshields

Morden,

Caraffini,

Kypraios

et al. 2023

International Journal of Intelligent Systems

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“…Rain traces or drops caused by outdoor weather are nonnegligible to computer vision, especially when capturing images or videos in the outdoor environment [1,2], because of the image quality degradation, leading to the unstable performance of other computer version algorithms [3][4][5][6]. Rain removal task is a primary but important component in basic algorithms, whose performance deeply affects the underlying algorithms based on rain removal models, such as target and segmentation detectors in camera-based autonomous driving or maritime video surveillance systems [7][8][9]. Although some deraining methods maintain high values of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSI M), corresponding algorithms are suitable for rain streak removal of a single image, even achieving only elementary real-time speed when an Nvidia Graphics Processing Unit (GPU) with high performance participates.…”

Section: Introductionmentioning

confidence: 99%

DAMNet: A Dual Adjacent Indexing and Multi-Deraining Network for Real-Time Image Deraining

et al. 2022

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Image deraining is increasingly critical in the domain of computer vision. However, there is a lack of fast deraining algorithms for multiple images without temporal and spatial features. To fill this gap, an efficient image-deraining algorithm based on dual adjacent indexing and multi-deraining layers is proposed to increase deraining efficiency. The deraining operation is based on two proposals: the dual adjacent method and the joint training method based on multi-deraining layers. The dual adjacent structure indexes pixels from adjacent features of the previous layer to merge with features produced by deraining layers, and the merged features are reshaped to prepare for the loss computation. Joint training method is based on multi-deraining layers, which utilise the pixelshuffle operation to prepare various deraining features for the multi-loss functions. Multi-loss functions jointly compute the structural similarity by loss calculation based on reshaped and deraining features. The features produced by the four deraining layers are concatenated in the channel dimension to obtain the total structural similarity and mean square error. During the experiments, the proposed deraining model is relatively efficient in primary rain datasets, reaching more than 200 fps, and maintains relatively impressive results in single and crossing datasets, demonstrating that our deraining model reaches one of the most advanced ranks in the domain of rain-removing.

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“…Moreover, many authors have successfully used deep learning (DL) or deep neural networks (DNN) to predict and estimate rain severity in vehicles [7], [8]. This serves as inspiration to leverage such algorithms to estimate rain conditions in order to improve our system's performance and reliability.…”

Section: Introductionmentioning

confidence: 99%

Automated Pest Detection with DNN on the Edge for Precision Agriculture

Albanese¹,

Nardello²,

Brunelli³

2021

Preprint

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Artificial intelligence has smoothly penetrated several economic activities, especially monitoring and control applications, including the agriculture sector. However, research efforts toward low-power sensing devices with fully functional machine learning (ML) on-board are still fragmented and limited in smart farming. Biotic stress is one of the primary causes of crop yield reduction. With the development of deep learning in computer vision technology, autonomous detection of pest infestation through images has become an important research direction for timely crop disease diagnosis. This paper presents an embedded system enhanced with ML functionalities, ensuring continuous detection of pest infestation inside fruit orchards. The embedded solution is based on a low-power embedded sensing system along with a Neural Accelerator able to capture and process images inside common pheromone-based traps. Three different ML algorithms have been trained and deployed, highlighting the capabilities of the platform. Moreover, the proposed approach guarantees an extended battery life thanks to the integration of energy harvesting functionalities. Results show how it is possible to automate the task of pest infestation for unlimited time without the farmer's intervention.

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Real-time rain severity detection for autonomous driving applications

Cited by 4 publications

References 20 publications

Driving in the Rain: A Survey toward Visibility Estimation through Windshields

Driving in the Rain: A Survey toward Visibility Estimation through Windshields

DAMNet: A Dual Adjacent Indexing and Multi-Deraining Network for Real-Time Image Deraining

Automated Pest Detection with DNN on the Edge for Precision Agriculture

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