Neural-Network-Based Traffic Sign Detection and Recognition in High-Definition Images Using Region Focusing and Parallelization

Avramović, Aleksej; Sluga, Davor; Tabernik, Domen; Skočaj, Danijel; Stojnić, Vladan; Ilc, Nejc

doi:10.1109/access.2020.3031191

Cited by 34 publications

(12 citation statements)

References 40 publications

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“…Another technique that detects traffic signs on night-time images [ 62 ] reaches an mAP of 0.94 on the DFG dataset, which is elaborated on in Section 4.7 . The method by Sasagawa et al [ 48 ] detects objects with an mAP of

on the SID dataset that contains images captured under low light (see Section 4.11 ).…”

Section: Discussionmentioning

confidence: 99%

“…For the sake of readability, we have presented the results of [ 61 , 72 ] in a separate table ( Table 4 ) because they reported results on their own introduced evaluation metrics. It is critical to emphasize that, apart from the five methods [ 62 , 63 , 64 , 66 , 68 ], all other approaches have not discussed their computational performances.…”

Section: Discussionmentioning

confidence: 99%

“…Another approach for detecting traffic signs is proposed by Avramovic et al [ 62 ]. The authors discussed that a driver could only focus in front of him and beside him through side mirrors during driving.…”

Section: Methodologiesmentioning

confidence: 99%

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Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments

Ahmed

Hashmi

Pagani

et al. 2021

Sensors

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Recent progress in deep learning has led to accurate and efficient generic object detection networks. Training of highly reliable models depends on large datasets with highly textured and rich images. However, in real-world scenarios, the performance of the generic object detection system decreases when (i) occlusions hide the objects, (ii) objects are present in low-light images, or (iii) they are merged with background information. In this paper, we refer to all these situations as challenging environments. With the recent rapid development in generic object detection algorithms, notable progress has been observed in the field of deep learning-based object detection in challenging environments. However, there is no consolidated reference to cover the state of the art in this domain. To the best of our knowledge, this paper presents the first comprehensive overview, covering recent approaches that have tackled the problem of object detection in challenging environments. Furthermore, we present a quantitative and qualitative performance analysis of these approaches and discuss the currently available challenging datasets. Moreover, this paper investigates the performance of current state-of-the-art generic object detection algorithms by benchmarking results on the three well-known challenging datasets. Finally, we highlight several current shortcomings and outline future directions.

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on the SID dataset that contains images captured under low light (see Section 4.11 ).…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments

Ahmed

Hashmi

Pagani

et al. 2021

Sensors

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“…This section talks about the approaches that have tackled object detection under strenuous conditions by exploiting the capabilities of Mask R-CNN. Another approach for detecting traffic signs is proposed by Avramovic et al [75]. The authors discussed that a driver could only focus in front of him and beside him through (side mirrors) during driving.…”

Section: Figure 12mentioning

confidence: 99%

Survey and Performance Analysis of Object Detection in Challenging Environments

Ahmed¹,

Ka²,

Pagani³

et al. 2021

Preprint

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Recent progress in deep learning has led to accurate and efficient generic object detection networks. Training of highly reliable models depends on large datasets with highly textured and rich images. However, in real-world scenarios, the performance of the generic object detection system decreases when (i) occlusions hide the objects, (ii) objects are present in low-light images, or (iii) they are merged with background information. In this paper, we refer to all these situations as challenging environments. With the recent rapid development in generic object detection algorithms, notable progress has been observed in the field of object detection in challenging environments. However, there is no consolidated reference to cover state-of-the-art in this domain. To the best of our knowledge, this paper presents the first comprehensive overview, covering recent approaches that have tackled the problem of object detection in challenging environments. Furthermore, we present the quantitative and qualitative performance analysis of these approaches and discuss the currently available challenging datasets. Moreover, this paper investigates the performance of current state-of-the-art generic object detection algorithms by benchmarking results on the three well-known challenging datasets. Finally, we highlight several current shortcomings and outline future directions.

show abstract

“…Therefore, an efficient algorithm is required to perform the processing and analysis of the captured image. Several machine learning algorithm has been proposed for traffic sign recognition include the TensorFlow transfer learning algorithm [8]- [10], AdaBoost algorithm [11], convolutional neural networks (CCN) algorithm [12], [13], fuzzy integral algorithm [14], neural network [15], artificial neural network (ANN) [16], deep learning [17], [18], color transformation [19], and texture feature extraction [20]. The types of algorithms used inside TensorFlow, such as transfer learning, increase the efficiency of the traffic sign recognition when compared to traditional machine learning.…”

Section: Introductionmentioning

confidence: 99%

Real-time traffic sign detection and recognition using Raspberry Pi

Isa

Yeong

Azyze

2022

IJECE

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Nowadays, the number of road accident in Malaysia is increasing expeditiously. One of the ways to reduce the number of road accident is through the development of the advanced driving assistance system (ADAS) by professional engineers. Several ADAS system has been proposed by taking into consideration the delay tolerance and the accuracy of the system itself. In this work, a traffic sign recognition system has been developed to increase the safety of the road users by installing the system inside the car for driver’s awareness. TensorFlow algorithm has been considered in this work for object recognition through machine learning due to its high accuracy. The algorithm is embedded in the Raspberry Pi 3 for processing and analysis to detect the traffic sign from the real-time video recording from Raspberry Pi camera NoIR. This work aims to study the accuracy, delay and reliability of the developed system using a Raspberry Pi 3 processor considering several scenarios related to the state of the environment and the condition of the traffic signs. A real-time testbed implementation has been conducted considering twenty different traffic signs and the results show that the system has more than 90% accuracy and is reliable with an acceptable delay.

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Neural-Network-Based Traffic Sign Detection and Recognition in High-Definition Images Using Region Focusing and Parallelization

Cited by 34 publications

References 40 publications

Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments

Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments

Survey and Performance Analysis of Object Detection in Challenging Environments

Real-time traffic sign detection and recognition using Raspberry Pi

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