Deep detection network for real-life traffic sign in vehicular networks

Yang, Tingting; Long, Xiang; Sangaiah, Arun Kumar; Zheng, Zhigao; Tong, Chao

doi:10.1016/j.comnet.2018.02.026

Cited by 89 publications

(79 citation statements)

References 15 publications

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“…The features from the deep layers have undergone several convolution operations and downsampling. Consequently, the information from these layers becomes deeply semantic yet more abstract [4,29,34,50]. Conversely, those features from the shallow layers are rich with precise positioning information for the object in the image but score low in semantic feature representation ability [41].…”

Section: Feature Fusionmentioning

confidence: 99%

“…Developing a driver-assistance system that would complement the driver skills by prompting about impending danger or right direction to maneuver ahead would go a long way in reducing the rate of traffic accidents [24,31,42]. Identifying road markings in images are tasking; this is due to the occlusion of the road markings by vehicles, variation in view angle, changes in illumination condition, complex background, poor image quality, and shadow [4,13,31,49]. Road-marking and traffic-sign detection systems are vital for driver-assistance systems and autonomous vehicles [6].…”

Section: Introductionmentioning

confidence: 99%

“…In recent times, there was a rapid advancement of deep learning technology. Convolutional neural networks and alongside some detection algorithms have shown great prospect in image detection and classification [4,16,50]. The successes recorded by AlexNet [17] and Zeiler and Fergus [18] inspired researchers towards utilizing the convolutional neural network as a feature extractor.…”

Section: Introductionmentioning

confidence: 99%

“…Detector Network. Initially, R-CNN relies on selective search algorithm to generate about 2000 regions of interest (RoIs)[4]. However, R-CNN is too slow for the real-time detection task.…”

mentioning

confidence: 99%

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Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network

Abubakar

Liu

et al. 2019

Journal of Sensors

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Pedestrians, motorist, and cyclist remain the victims of poor vision and negligence of human drivers, especially in the night. Millions of people die or sustain physical injury yearly as a result of traffic accidents. Detection and recognition of road markings play a vital role in many applications such as traffic surveillance and autonomous driving. In this study, we have trained a nighttime road-marking detection model using NIR camera images. We have modified the VGG-16 base network of the state-of-the-art faster R-CNN algorithm by using a multilayer feature fusion technique. We have demonstrated another promising feature fusion technique of concatenating all the convolutional layers within a stage to extract image features. The modification boosts the overall detection performance of the model by utilizing the advantages of the shallow layers and the deep layers of the VGG-16 network. The training samples were augmented using random rotation and translation to enhance the heterogeneity of the detection algorithm. We have achieved a mean average precision (mAP) of 89.48% and 92.83% for the baseline faster R-CNN and our modified method, respectively.

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Section: Feature Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Detector Network. Initially, R-CNN relies on selective search algorithm to generate about 2000 regions of interest (RoIs)[4]. However, R-CNN is too slow for the real-time detection task.…”

mentioning

confidence: 99%

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Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network

Abubakar

Liu

et al. 2019

Journal of Sensors

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“…Therefore, it was of importance that parallel algorithm is applied in the gene transcription for complex biological phenomena. 36,37…”

Section: Parallel Analysismentioning

confidence: 99%

Parallel generated method of transcriptional regulatory networks

Liu

et al. 2018

Concurrency and Computation

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Generated method of transcriptional regulatory networks remains an important research in biology. Many approaches have been proposed to construct transcriptional regulatory networks.However, with the increase of ChIP-seq and RNA-seq data, the speed of constructing transcriptional regulation networks is still a challenge. Moreover, parallel computing lacks application in constructing gene regulatory networks through the analysis of the relationships between transcription factors (TFs) and target genes (TGs). Therefore, in this paper, a parallel generated method of transcriptional regulatory networks was proposed. First, two datasets, Michigan Cancer Foundation -7 (MCF-7) and Cardiomyocytes (CM) were applied. Then, a parallel method was used to generate transcriptional regulatory network with their transcription factors (TFs) and target genes (TGs). Finally, experimental results showed that 61% regulatory relations in MCF-7 were validated in the Gene Expression Omnibus (GEO), while 29% results needed further experimental verification. Besides, 56% regulatory relations in CM were consistent with GEO, while 33% results were not yet verified. Furthermore, speed of parallel algorithm was faster than traditional serial algorithm in generating transcriptional regulatory networks.

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