Traffic sign detection method based on Faster R-CNN

Wu, Linxiu; Li, Houjie; He, Jianjun; Chen, Xuan

doi:10.1088/1742-6596/1176/3/032045

Cited by 8 publications

(8 citation statements)

References 8 publications

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“…From Table 6 , the proposed algorithm achieves a 275% improvement in recognition speed and has greater values of the recall rate, mAP, AP 50 and AP 75 , compared with the two-stage traffic sign recognition algorithm (modified Faster R-CNN [ 9 ]). In addition, the proposed algorithm achieves 3.1% increase in the recall rate, 5.4% increase in the mAP, 3.3% increase in the AP 50 and 7.4% increase in the AP 75 , compared with one of the strongest competitors in the one-stage traffic sign recognition algorithm (i.e., modified YOLOv3 [ 10 ]).…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Wu et al [9] propose a two-stage traffic sign recognition method based on modified Faster R-CNN, which absorbs the characteristics of SPP-Net [21] and increases the depth of the network based on R-CNN [22]. Furthermore, it is proposed to use the region recommendation network to extract the recognition area and share the features of the convolution layer with the whole recognition network, which further improves the recognition accuracy.…”

Section: The State-of-the-art Traffic Sign Recognition Algorithmmentioning

confidence: 99%

“… Visual comparison of recognition performance of different traffic sign recognition algorithms for the same image. ( a – f ) are the recognition results of the traffic sign recognition algorithms based on modified Faster R-CNN [ 9 ], modified SSD [ 23 ], modified RefineDet [ 26 ], modified RetinaNet101 [ 39 ] (its backbone is ResNet-101), modified YOLOv3 [ 10 ] and the proposed method. …”

Section: Figurementioning

confidence: 99%

“…However, the traditional traffic sign recognition method suffers from the disadvantages of poor recognition performance and network robustness. Although the two-stage traffic sign recognition method (such as modified Faster R-CNN [ 9 ]) is proposed, which can achieve high recognition accuracy, its recognition speed is very slow and cannot meet the real-time requirements. The one-stage traffic sign recognition network (such as modified YOLOv3 [ 10 ]) can achieve high recognition speed, but its recognition effect is not accurate enough.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Multiscale Recognition Method for the Optimization of Traffic Signs Using GMM and Category Quality Focal Loss

Gao

Chen

Shi

et al. 2020

Sensors

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Effective traffic sign recognition algorithms can assist drivers or automatic driving systems in detecting and recognizing traffic signs in real-time. This paper proposes a multiscale recognition method for traffic signs based on the Gaussian Mixture Model (GMM) and Category Quality Focal Loss (CQFL) to enhance recognition speed and recognition accuracy. Specifically, GMM is utilized to cluster the prior anchors, which are in favor of reducing the clustering error. Meanwhile, considering the most common issue in supervised learning (i.e., the imbalance of data set categories), the category proportion factor is introduced into Quality Focal Loss, which is referred to as CQFL. Furthermore, a five-scale recognition network with a prior anchor allocation strategy is designed for small target objects i.e., traffic sign recognition. Combining five existing tricks, the best speed and accuracy tradeoff on our data set (40.1% mAP and 15 FPS on a single 1080Ti GPU), can be achieved. The experimental results demonstrate that the proposed method is superior to the existing mainstream algorithms, in terms of recognition accuracy and recognition speed.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: The State-of-the-art Traffic Sign Recognition Algorithmmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Multiscale Recognition Method for the Optimization of Traffic Signs Using GMM and Category Quality Focal Loss

Gao

Chen

Shi

et al. 2020

Sensors

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“…In recent years, many CNN-based studies have focused on such problems as small object detection [6], face detection [20], crowd counting [21,22], traffic sign detection [23], and car detection [24]. In the meantime, there are increasing research on the deep learning application for the humanoid robot's object recognition, grasp detection, etc.…”

Section: Researches On Robot Recognition and Object Detectionmentioning

confidence: 99%

Optimized Convolutional Neural Network-Based Object Recognition for Humanoid Robot

Xiao¹,

Zeng²,

Yuan³

2020

J Robotics Autom

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In recent years, many researchers have proposed a series of algorithms based on convolutional neural networks and achieved good performances in the field of object detection and recognition. For humanoid robots, they are designed to assist or replace people in completing a series of anthropomorphic tasks, and their ability to recognize and grasp surrounding objects is the most basic requirement. Therefore, this paper proposes an algorithm based on the optimized convolutional neural network (CNN) and can be used to detect and classify the objects by humanoid robots. Particularly, Faster region-based CNN (Faster R-CNN) is used for detecting the objects and the original network is refined by feature concatenation strategy. The feature fusion layer is also proposed to make the feature map contain more information, the improved non-maximum suppression (NMS) algorithm is adopted to solve the overlapping object detection problem, and some layers are also modified in the original network to get faster speed and smaller network size. The recognition and grasping performance of the humanoid robots are tested by the proposed algorithms in a virtual simulation environment, and the experiment results show that the proposed algorithms are feasible and perform well.

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TAFFNet: Two-Stage Attention-Based Feature Fusion Network for Surface Defect Detection

Cao

Yang

2022

J Sign Process Syst

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In recent years, deep learning has made significant progress in wood panel defect detection. However, there are still challenges such as low detection , slow detection speed, and difficulties in deploying embedded devices on wood panel surfaces. To overcome these issues, we propose a lightweight wood panel defect detection method called YOLOv5-LW, which incorporates attention mechanisms and a feature fusion network. Firstly, to enhance the detection capability of acceptable defects, we introduce the Multi-scale Bi-directional Feature Pyramid Network (MBiFPN) as a feature fusion network. The MBiFPN reduces feature loss, enriches local and detailed features, and improves the model's detection capability for acceptable defects. Secondly, to achieve a lightweight design, we reconstruct the ShuffleNetv2 network model as the backbone network. This reconstruction reduces the number of parameters and computational requirements while maintaining performance. We also introduce the Stem Block and Spatial Pyramid Pooling Fast (SPPF) models to compensate for any accuracy loss resulting from the lightweight design, ensuring the model's detection capabilities remain intact while being computationally efficient. Thirdly, we enhance the backbone network by incorporating Efficient Channel Attention (ECA), which improves the network's focus on key information relevant to defect detection. By attending to essential features, the model becomes more proficient in accurately identifying and localizing defects. We validate the proposed method using a selfdeveloped wood panel defect dataset. The experimental results demonstrate the effectiveness of the improved YOLOv5-LW method. Compared to the original model, our approach achieves a 92.8% accuracy rate, reduces the number of parameters by 27.78%, compresses computational volume by 41.25%, improves detection inference speed by 10.16%, and enhances the detection accuracy of two types of acceptable defects (dead knots and cracks) by 0.2% and 1.3% respectively.

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Traffic sign detection method based on Faster R-CNN

Cited by 8 publications

References 8 publications

A Multiscale Recognition Method for the Optimization of Traffic Signs Using GMM and Category Quality Focal Loss

A Multiscale Recognition Method for the Optimization of Traffic Signs Using GMM and Category Quality Focal Loss

Optimized Convolutional Neural Network-Based Object Recognition for Humanoid Robot

TAFFNet: Two-Stage Attention-Based Feature Fusion Network for Surface Defect Detection

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