Multi-Scale Dilated Convolution Network Based Depth Estimation in Intelligent Transportation Systems

Tian, Yanling; Zhang, Qieshi; Ren, Ziliang; Wu, Fangming; Hao, Pin; Hu, Jinglu

doi:10.1109/access.2019.2960520

Cited by 12 publications

(7 citation statements)

References 53 publications

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“…For vehicle make and model recognition, we have found that Xception has out-performed other state-of-the-art deep learning architectures as shown in Table 3. Model Accuracy ResNet-152 [29] 92% Inception-ResNet-v2 91.3% Xception [24] 92.45% DenseNet-201 [27] 91.3% MobileNet-v1 [28] 87.6% DenseNet-121 89.7%…”

Section: Evaluation and Resultsmentioning

confidence: 99%

An Empirical Analysis of Deep Learning Architectures for Vehicle Make and Model Recognition

et al. 2021

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Vehicle make and model recognition is an important component of the Intelligent Transport System (ITS), which plays an essential role in vehicle surveillance and traffic monitoring. In this paper, we evaluated the performance of the recent deep neural networks for vehicle recognition to identify 196 different types of vehicles based on their make, model, and year using Stanford Cars data. Transfer learning has been employed to reduce the training time and also added a dropout of 0.5 at the last dense layer before the output layer for improved generalization performance on an applied dataset. Different augmentation methods such as random erasing, horizontal flip, random crop, resize, rotate, shear, and sharpen were applied to the training dataset to increase the number of images. Besides, optimization methods namely mixup and cyclical learning rate were also investigated to train these deep neural networks to avoid overfitting. Finally, the proposed ensemble learning framework using k-fold cross-validation has resulted in the improved accuracy of image classification for the vehicle make and model recognition. The final achieved accuracy is 93.96% through ensembling homogeneous models of DenseNet201, which is higher than previous stateof-the-art approaches for the vehicle make and model recognition. Simultaneously, it has also improved the performance of other models where MobileNetV2 has shown the highest improvement with an increase of 2.8% in the overall accuracy using the proposed ensemble learning technique.INDEX TERMS deep neural networks, vehicle make and model recognition, ensemble learning, and k-fold cross-validation

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Section: Evaluation and Resultsmentioning

confidence: 99%

An Empirical Analysis of Deep Learning Architectures for Vehicle Make and Model Recognition

et al. 2021

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“…It is a separate end-to-end network that turns target detection into a regression problem. To be more specific, the method of regression and the CNN [44,45] are used to replace the sliding window of the traditional target detection to realize the feature extraction of the driver's face. This method of feature extraction is less affected by the external environment and has the advantage of extracting target features quickly.…”

Section: A Face Detection Based On the Improved Yolov3-tiny Networkmentioning

confidence: 99%

A Fatigue Driving Detection Algorithm Based on Facial Multi-Feature Fusion

Gong

Ren

2020

IEEE Access

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Researches on machine vision-based driver fatigue detection algorithm have improved traffic safety significantly. Generally, many algorithms do not analyze driving state from driver characteristics. It results in some inaccuracy. The paper proposes a fatigue driving detection algorithm based on facial multifeature fusion combining driver characteristics. First, we introduce an improved YOLOv3-tiny convolutional neural network to capture the facial regions under complex driving conditions, eliminating the inaccuracy and affections caused by artificial feature extraction. Second, on the basis of the Dlib toolkit, we introduce the Eye Feature Vector(EFV) and Mouth Feature Vector(MFV), which are the evaluation parameters of the driver's eye state and mouth state, respectively. Then, the driver identity information library is constructed by offline training, including driver eye state classifier library, driver mouth state classifier library, and driver biometric library. Finally, we construct the driver identity verification model and the driver fatigue assessment model by online assessment. After passing the identity verification, calculate the driver's closed eyes time, blink frequency and yawn frequency to evaluate the driver's fatigue state. In simulated driving applications, our algorithm detects the fatigue state at a speed of over 20fps with an accuracy of 95.10%.

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“…Recently, depth estimation has also become a popular task in intelligent transportation systems, since depth maps can be exploited in scene understanding and environment perception, particularly in a self-driving scenario. More specifically, Tian et al proposed a multiscale dilated convolution network to end the issue of fine detail loss (31). Park et al worked on devising a deep sensor fusion framework to result in more accurate consequences (32).…”

Section: Related Workmentioning

confidence: 99%

Novel Hybrid Neural Network for Dense Depth Estimation using On-Board Monocular Images

Jia

Pei

Yang

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Depth information from still 2D images plays an important role in automated driving, driving safety, and robotics. Monocular depth estimation is considered as an ill-posed and inherently ambiguous problem in general, and a tight issue is how to obtain global information efficiently since pure convolutional neural networks (CNNs) merely extract the local information. To end that, some previous works utilized conditional random fields (CRFs) to obtain the global information, but it is notoriously difficult to optimize. In this paper, a novel hybrid neural network is proposed to solve that, and concurrently a dense depth map is predicted from the monocular still image. Specifically: first, the deep residual network is utilized to obtain multi-scale local information and then feature correlation (FCL) blocks are used to correlate these features. Finally, the feature selection attention-based mechanism is adopted to fuse the multi-layer features, and the multi-layer recurrent neural networks (RNNs) are utilized with bidirectional long short-term memory (Bi-LSTM) unit as the output layer. Furthermore, a novel logarithm exponential average error (LEAE) is proposed to overcome over-weighted problem. The multi-scale feature correlation network (MFCN) is evaluated on large-scale KITTI benchmarks (LKT), which is a subset of KITTI raw dataset, and NYU depth v2. The experiments indicate that the proposed unified network outperforms existing methods. This method also updates the state-of-the-art performance on LKT datasets. Importantly, the depth estimation method can be widely used for collision risk assessment and avoidance in driving assistance systems or automated pilot systems to achieve safety in a more economical and convenient way.

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Multi-Scale Dilated Convolution Network Based Depth Estimation in Intelligent Transportation Systems

Cited by 12 publications

References 53 publications

An Empirical Analysis of Deep Learning Architectures for Vehicle Make and Model Recognition

An Empirical Analysis of Deep Learning Architectures for Vehicle Make and Model Recognition

A Fatigue Driving Detection Algorithm Based on Facial Multi-Feature Fusion

Novel Hybrid Neural Network for Dense Depth Estimation using On-Board Monocular Images

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