Traffic Sign Detection Algorithm based on improved YOLOv4

Wang, Huibai; Yu, Hao

doi:10.1109/itaic49862.2020.9339181

Cited by 11 publications

(3 citation statements)

References 4 publications

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“…However, two-stage algorithms like R-CNN suffer from slower detection speeds. To address this, Wang et al [14] fused feature layers in YOLOv4 to improve accuracy. One-stage detection models have become popular, narrowing the accuracy gap with two-stage models.…”

Section: Related Workmentioning

confidence: 99%

Research on traffic sign detection based on improved YOLOv4

Cao,

Li,

Zhang

et al. 2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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Traffic signs contain important traffic information. The traditional traffic sign detection method cannot solve the problem of low detection accuracy caused by the small, occupied area of traffic sign images. Based on this, a traffic sign detection algorithm based on improved YOLOv4 is proposed. Firstly, the 13 × 13 large receptive field detection layer is removed on the YOLOv4 structure, and the 104 × 104 detection layer is added. It obtains more global feature information and improves detection accuracy. The attention mechanism is introduced into the algorithm, that is, the backbone network extracts three feature layers and then adds the scSE module. Make the network focus on the target area and improve the algorithm detection ability. Secondly, in order to speed up the convergence of the network and improve the detection accuracy, a dynamic residual connection is added to the backbone network. It promotes the spread of well-performing signals. And use the decoupled-head detection head to use different branches to calculate classification and positioning tasks. By evaluating the average accuracy of the detection effect of CCTSDB traffic sign data set, mAP reaches 97.68 %, which is 3.78 % higher than YOLOv4. Moreover, network convergence experiments have shown that the improved model converges faster. Compared with other models, the improved model has better detection performance for smaller traffic signs and can better meet the actual needs of high-precision detection.

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Section: Related Workmentioning

confidence: 99%

Research on traffic sign detection based on improved YOLOv4

Cao,

Li,

Zhang

et al. 2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

View full text Add to dashboard Cite

show abstract

“…Since the detection speed of YOLO series detectors met the real-time detection requirements of traffic signs, many scholars have conducted research on YOLO. The [32] optimized the network structure of YOLOv4. This method adds a layer of feature pyramid to detect multi-scale features and uses K-means to obtain multi-scale corresponding anchor boxes, which improves the localization accuracy.…”

Section: Traffic Sign Detectionmentioning

confidence: 99%

Attention feature fusion network for small traffic sign detection

Wu¹,

Yang²,

Zhang³

et al. 2022

Eng. Res. Express

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Object detection has made great progress with the rise of convolutional neural networks in recent years. Traffic sign detection is a research hotspot for object detection tasks. The existing detection models have the problems of inaccurate positioning and low classification accuracy when detecting small traffic signs. To address these issues, in this paper, we propose a small traffic sign detection method based on YOLOv4. Specifically, we design an attention-based feature fusion module including attention spatial pyramid pooling (ASPP) and attention path aggregation networks (APAN). ASPP highlights useful small object information and suppresses invalid interference information in the background. APAN reduces information loss during feature fusion. A large number of experimental results on public datasets show that the method in this paper improves the detection performance of the model. In terms of small traffic sign detection, the method improves YOLOv4 by 12 mAP, and meets the real-time requirements of automatic driving detection (more than 50 FPS).

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“…This modification results in improved detection accuracy along with increased detection speed. Wang et al [37] developed a lightweight YOLOv4 algorithm by replacing the backbone with a lightweight architecture, MobileNetv2 [38], and attention mechanisms were also employed to enhance the model's detection capabilities. In summary, DL-based traffic sign detection algorithms are characterized by increased robustness and higher detection accuracy compared with traditional methods.…”

Section: Introductionmentioning

confidence: 99%

YOLOv7-TS: A Traffic Sign Detection Model Based on Sub-Pixel Convolution and Feature Fusion

Zhao,

Yuan,

et al. 2024

Sensors

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In recent years, significant progress has been witnessed in the field of deep learning-based object detection. As a subtask in the field of object detection, traffic sign detection has great potential for development. However, the existing object detection methods for traffic sign detection in real-world scenes are plagued by issues such as the omission of small objects and low detection accuracies. To address these issues, a traffic sign detection model named YOLOv7-Traffic Sign (YOLOv7-TS) is proposed based on sub-pixel convolution and feature fusion. Firstly, the up-sampling capability of the sub-pixel convolution integrating channel dimension is harnessed and a Feature Map Extraction Module (FMEM) is devised to mitigate the channel information loss. Furthermore, a Multi-feature Interactive Fusion Network (MIFNet) is constructed to facilitate enhanced information interaction among all feature layers, improving the feature fusion effectiveness and strengthening the perception ability of small objects. Moreover, a Deep Feature Enhancement Module (DFEM) is established to accelerate the pooling process while enriching the highest-layer feature. YOLOv7-TS is evaluated on two traffic sign datasets, namely CCTSDB2021 and TT100K. Compared with YOLOv7, YOLOv7-TS, with a smaller number of parameters, achieves a significant enhancement of 3.63% and 2.68% in the mean Average Precision (mAP) for each respective dataset, proving the effectiveness of the proposed model.

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Traffic Sign Detection Algorithm based on improved YOLOv4

Cited by 11 publications

References 4 publications

Research on traffic sign detection based on improved YOLOv4

Research on traffic sign detection based on improved YOLOv4

Attention feature fusion network for small traffic sign detection

YOLOv7-TS: A Traffic Sign Detection Model Based on Sub-Pixel Convolution and Feature Fusion

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