Single Image Water Hazard Detection Using FCN with Reflection Attention Units

Han, Xiaofeng; Nguyen, Chuong; You, Shaodi; Lu, Jianfeng

doi:10.1007/978-3-030-01231-1_7

Cited by 29 publications

(38 citation statements)

References 19 publications

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“…As for the discriminator, the generated binary images or the masks representing ground truth are fed to be discriminated and the corresponding original images are also fed as conditions. As far as we know, we are the first to use cGAN to deal with such water hazard detection tasks and our method based on cGAN outperforms the method proposed by Han et al [2], which is the state-of-theart over their released water hazard dataset 'Puddle-1000'.…”

Section: Introductionmentioning

confidence: 92%

“…Laser Radar and dual-polarized camera sometimes can be useful to deal with land-surface classification tasks in autonomous driving, but those devices are too expensive and do not provide sufficient detection accuracy [2]. The developing theories and technologies of image processing and computer vision make it possible to detect water hazards from single color image.…”

Section: Introductionmentioning

confidence: 99%

“…For better performance, we take advantage of Reflection Attention Units (RAU) proposed in [2]. RAU is a structure consisting of a preprocessing function and a convolutional layer.…”

Section: Introductionmentioning

confidence: 99%

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Water Hazard Detection Using Conditional Generative Adversarial Network With Mixture Reflection Attention Units

Wang

2019

IEEE Access

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Water hazard detection is an important yet challenging task in autonomous driving as the complex underwater geography brings many hidden risks, e.g. puddles, which could make self-driving cars unsafe. Fully convolutional networks (FCN) have achieved remarkable performance on many image segmentation tasks, but water hazard detection problems are always hard to deal with due to the reflection characteristic of water. In this paper, we use Conditional Generative Adversarial Networks (cGAN) to deal with the water hazard detection. It has been proved that the Reflection Attention Unit (RAU) can improve the performance of deep networks for water hazard detection when added into the deep networks. We take advantage of RAU and carefully investigate its effect when placed in different layers of cGAN, with the best configuration being our proposed method: cGAN-mRAU. The 'Puddle-1000' dataset is employed to evaluate our method. We use two subsets respectively and combine them together. We randomly choose some images and their ground-truth masks to train the model, and we use other images to test the model. We find many annotation mistakes in the dataset and correct them through re-annotation. Compared with FCN-8s with focal loss and 5 RAUs (FCN-8s-FL-5RAU), which is the state-of-the-art over 'Puddle-1000', both cGAN and cGAN-mRAU outperform the FCN-8s-FL-5RAU in F1-measure, where cGAN achieves the best performance on 'Off Road' subset and cGAN-mRAU achieves the best performance on 'On Road' subset as well as whole dataset.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Water Hazard Detection Using Conditional Generative Adversarial Network With Mixture Reflection Attention Units

Wang

2019

IEEE Access

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“…For example, Zhan et al [13] proposed an online learning approach to recognize the water region for the USV in the unknown navigation environment using a convolutional neural network (CNN). Han et al [14] innovatively used the Fully Connected Convolutional Network (FCN) to achieve water hazards detection on the road. Despite of high accuracy, the artificial neural network with complex structure needs to be pre-trained in many scenes before use and requires high computing power.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of River Scenes Based on Water Surface Reflection Mechanism

Lin

Zhu

et al. 2020

Applied Sciences

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Segmentation of a river scene is a representative case of complex image segmentation. Different from road segmentation, river scenes often have unstructured boundaries and contain complex light and shadow on the water’s surface. According to the imaging mechanism of water pixels, this paper designed a water description feature based on a multi-block local binary pattern (MB-LBP) and Hue variance in HSI color space to detect the water region in the image. The improved Local Binary Pattern (LBP) feature was used to recognize the water region and the local texture descriptor in HSI color space using Hue variance was used to detect the shadow area of the river surface. Tested on two data sets including simple and complex river scenes, the proposed method has better segmentation performance and consumes less time than those of two other widely used methods.

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“…Evidently, the model trained with different loss functions will have a vast difference between recall rate and precision. Before our project, we conduct an experiment on a water puddle segmentation dataset [8]. We find that the model trained with focal loss possesses a higher precision while the model trained with cross-entropy loss has a higher recall rate, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Importance-Aware Semantic Segmentation with Efficient Pyramidal Context Network for Navigational Assistant Systems

Xiang

Wang

Yang

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Semantic Segmentation (SS) is a task to assign semantic label to each pixel of the images, which is of immense significance for autonomous vehicles, robotics and assisted navigation of vulnerable road users. It is obvious that in different application scenarios, different objects possess hierarchical importance and safety-relevance, but conventional loss functions like cross entropy have not taken the different levels of importance of diverse traffic elements into consideration. To address this dilemma, we leverage and re-design an importance-aware loss function, throwing insightful hints on how importance of semantics are assigned for real-world applications. To customize semantic segmentation networks for different navigational tasks, we extend ERF-PSPNet, a realtime segmenter designed for wearable device aiding visually impaired pedestrians, and propose BiERF-PSPNet, which can yield high-quality segmentation maps with finer spatial details exceptionally suitable for autonomous vehicles. A comprehensive variety of experiments with these efficient pyramidal context networks on CamVid and Cityscapes datasets demonstrates the effectiveness of our proposal to support diverse navigational assistant systems.

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Single Image Water Hazard Detection Using FCN with Reflection Attention Units

Cited by 29 publications

References 19 publications

Water Hazard Detection Using Conditional Generative Adversarial Network With Mixture Reflection Attention Units

Water Hazard Detection Using Conditional Generative Adversarial Network With Mixture Reflection Attention Units

Segmentation of River Scenes Based on Water Surface Reflection Mechanism

Importance-Aware Semantic Segmentation with Efficient Pyramidal Context Network for Navigational Assistant Systems

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