Learning Region-Based Attention Network for Traffic Sign Recognition

Zhou, Ke; Zhan, Yufei; Fu, Dongmei

doi:10.3390/s21030686

Cited by 39 publications

(21 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Then, a deep neural network was used to classify the point cloud projection on RGB images. Zhou et al [41] proposed an ice environment traffic sign recognition benchmark (ITSRB) and detection benchmark (ITSDB) marked in the COCO2017 format. In addition, a highresolution traffic sign classification (PFANet) based on attention network was proposed, and ablation research was performed on the design parallel fusion attention module.…”

mentioning

confidence: 99%

A Novel Neural Network Model for Traffic Sign Detection and Recognition under Extreme Conditions

Wan

Gao

et al. 2021

Journal of Sensors

View full text Add to dashboard Cite

Traffic sign detection is extremely important in autonomous driving and transportation safety systems. However, the accurate detection of traffic signs remains challenging, especially under extreme conditions. This paper proposes a novel model called Traffic Sign Yolo (TS-Yolo) based on the convolutional neural network to improve the detection and recognition accuracy of traffic signs, especially under low visibility and extremely restricted vision conditions. A copy-and-paste data augmentation method was used to build a large number of new samples based on existing traffic-sign datasets. Based on You Only Look Once (YoloV5), the mixed depth-wise convolution (MixConv) was employed to mix different kernel sizes in a single convolution operation, so that different patterns with various resolutions can be captured. Furthermore, the attentional feature fusion (AFF) module was integrated to fuse the features based on attention from same-layer to cross-layer scenarios, including short and long skip connections, and even performing the initial fusion with itself. The experimental results demonstrated that, using the YoloV5 dataset with augmentation, the precision was 71.92, which was increased by 34.56 compared with the data without augmentation, and the mean average precision mAP_0.5 was 80.05, which was increased by 33.11 compared with the data without augmentation. When MixConv and AFF were applied to the TS-Yolo model, the precision was 74.53 and 2.61 higher than that with data augmentation only, and the value of mAP_0.5 was 83.73 and 3.68 higher than that based on the YoloV5 dataset with augmentation only. Overall, the performance of the proposed method was competitive with the latest traffic sign detection approaches.

show abstract

mentioning

confidence: 99%

A Novel Neural Network Model for Traffic Sign Detection and Recognition under Extreme Conditions

Wan

Gao

et al. 2021

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…Many researchers have attempted to increase the accuracy of traffic sign recognition using a variety of methods [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. In general, there are three categories of traffic signs in China: indication, warning and prohibition, which are represented by blue, yellow and red, respectively.…”

Section: Related Workmentioning

confidence: 99%

Visual Recognition of Traffic Signs in Natural Scenes Based on Improved RetinaNet

Liu

Cai

Tang

et al. 2022

Entropy

View full text Add to dashboard Cite

Aiming at recognizing small proportion, blurred and complex traffic sign in natural scenes, a traffic sign detection method based on RetinaNet-NeXt is proposed. First, to ensure the quality of dataset, the data were cleaned and enhanced to denoise. Secondly, a novel backbone network ResNeXt was employed to improve the detection accuracy and effection of RetinaNet. Finally, transfer learning and group normalization were adopted to accelerate our network training. Experimental results show that the precision, recall and mAP of our method, compared with the original RetinaNet, are improved by 9.08%, 9.09% and 7.32%, respectively. Our method can be effectively applied to traffic sign detection.

show abstract

“…In this short communication, we propose a feasible solution for heavy goods vehicle detection. Computer Vision algorithms have been implemented for various tasks in traffic monitoring for many years, e.g., traffic sign recognition [1][2][3][4][5][6][7]; intelligent traffic light system [8]; vehicle speed monitoring [9]; traffic violation monitoring [10]; vehicle tracking [11][12][13]; vehicle classification [14][15][16][17][18][19][20][21][22][23][24][25][26]; vehicle counting system on streets and highways [27][28][29][30][31]; parking spot detection from the point of view of the car for parking assistants [32,33]; and parking spot monitoring [34][35][36][37][38][39][40][41][42][43][44][45][46][47]…”

Section: Introductionmentioning

confidence: 99%

Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

et al. 2021

View full text Add to dashboard Cite

The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection.

show abstract

Learning Region-Based Attention Network for Traffic Sign Recognition

Cited by 39 publications

References 28 publications

A Novel Neural Network Model for Traffic Sign Detection and Recognition under Extreme Conditions

A Novel Neural Network Model for Traffic Sign Detection and Recognition under Extreme Conditions

Visual Recognition of Traffic Signs in Natural Scenes Based on Improved RetinaNet

Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

Contact Info

Product

Resources

About