Dilated Fully Convolutional Neural Network for Depth Estimation from a Single Image

Hua, Yindong; Liu, Yifeng; Li, Binghan; Lu, Mi

doi:10.1109/csci49370.2019.00115

Cited by 8 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, neural network has made significant progress in numerous computer vision tasks [22] [23] [24] and natural language processing tasks [25] [26] [27]. Various Convolutional Neural Networks (CNNs) are designed to obtain a more accurate estimation of transmission matrix t(x) by self-learning the mapping between hazy images and corresponding transmission maps, which outperform most conventional dehazing algorithms.…”

Section: Overview Of Cnn-based Dehazing Algorithmsmentioning

confidence: 99%

Advanced Multiple Linear Regression Based Dark Channel Prior Applied on Dehazing Image and Generating Synthetic Haze

Li¹,

Hua²,

Lu³

2021

Adv. sci. technol. eng. syst. j.

Self Cite

View full text Add to dashboard Cite

Haze removal is an extremely challenging task, and object detection in the hazy environment has recently gained much attention due to the popularity of autonomous driving and traffic surveillance. In this work, the authors propose a multiple linear regression haze removal model based on a widely adopted dehazing algorithm named Dark Channel Prior. Training this model with a synthetic hazy dataset, the proposed model can reduce the unanticipated deviations generated from the rough estimations of transmission map and atmospheric light in Dark Channel Prior. To increase object detection accuracy in the hazy environment, the authors further present an algorithm to build a synthetic hazy COCO training dataset by generating the artificial haze to the MS COCO training dataset. The experimental results demonstrate that the proposed model obtains higher image quality and shares more similarity with ground truth images than most conventional pixel-based dehazing algorithms and neural network based haze-removal models. The authors also evaluate the mean average precision of Mask R-CNN when training the network with synthetic hazy COCO training dataset and preprocessing test hazy dataset by removing the haze with the proposed dehazing model. It turns out that both approaches can increase the object detection accuracy significantly and outperform most existing object detection models over hazy images.

show abstract

Section: Overview Of Cnn-based Dehazing Algorithmsmentioning

confidence: 99%

Advanced Multiple Linear Regression Based Dark Channel Prior Applied on Dehazing Image and Generating Synthetic Haze

Li¹,

Hua²,

Lu³

2021

Adv. sci. technol. eng. syst. j.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This paper is an extension of work originally presented in conference name [1]. Depth prediction has always been a core task to understand the geometric relations within a 3D scene.…”

Section: Introductionmentioning

confidence: 99%

Dilated Fully Convolutional Neural Network for Depth Estimation from a Single Image

Li¹,

Hua²,

Liu³

et al. 2021

Adv. sci. technol. eng. syst. j.

Self Cite

View full text Add to dashboard Cite

Depth prediction plays a key role in understanding a 3D scene. Several techniques have been developed throughout the years, among which Convolutional Neural Network has recently achieved state-of-the-art performance on estimating depth from a single image. However, traditional CNNs suffer from the lower resolution and information loss caused by the pooling layers. And oversized parameters generated from fully connected layers often lead to a exploded memory usage problem. In this paper, we present an advanced Dilated Fully Convolutional Neural Network to address the deficiencies. Taking advantages of the exponential expansion of the receptive field in dilated convolutions, our model can minimize the loss of resolution. It also reduces the amount of parameters significantly by replacing the fully connected layers with the fully convolutional layers. We show experimentally on NYU Depth V2 datasets that the depth prediction obtained from our model is considerably closer to ground truth than that from traditional CNNs techniques.

show abstract

“…In recent years, computer vision-based system has been playing an important role in a wide application field of urban traffic, such as autonomous and assisted driving, traffic monitoring system and security maintenance. Neural network has made significant breakthrough on some fields, such as computer vision [8] [11] [12] and speech recognition [22] [23] [24]. With the rapid development of object detection and recognition techniques, and the increasing number of traffic cameras, smart cities are becoming more intelligent and safer.…”

Section: Introductionmentioning

confidence: 99%

Object Detection in Hazy Environment Enhanced by Preprocessing Image Dataset with Synthetic Haze

Hua

2020

2020 International Conference on Computational Science and Computational Intelligence (CSCI)

Self Cite

View full text Add to dashboard Cite

Object detection in hazy environment has always been a difficult task in the autonomous driving field. Huge breakthrough is hard to achieve due to the lack of large-scale hazy image dataset with detailed labels. In this work, we present a simple and flexible algorithm to generate synthetic haze to MS COCO training dataset, which aims to enhance the performance of object detection in haze when taking the new synthesized hazy images as training dataset. Our algorithm is inspired by the Multiple Linear Regression Dark Channel Prior (MLDCP), and we obtain a general model that can add synthetic haze to haze-free images by implementing Stochastic Gradient Descent (SGD) to the reversed MLDCP model. We further evaluate the mean average precision (mAP) of Mask R-CNN when we train the network with the Hazy-COCO training dataset and preprocessing test hazy dataset with existing single image dehazing algorithms.

show abstract

Dilated Fully Convolutional Neural Network for Depth Estimation from a Single Image

Cited by 8 publications

References 16 publications

Advanced Multiple Linear Regression Based Dark Channel Prior Applied on Dehazing Image and Generating Synthetic Haze

Advanced Multiple Linear Regression Based Dark Channel Prior Applied on Dehazing Image and Generating Synthetic Haze

Dilated Fully Convolutional Neural Network for Depth Estimation from a Single Image

Object Detection in Hazy Environment Enhanced by Preprocessing Image Dataset with Synthetic Haze

Contact Info

Product

Resources

About