A Traffic Sign Recognition Method Based on Improved YOLOv3

Fan, Wenshuo; Yi, Nanqiao; Hu, Yueyun

doi:10.1007/978-3-030-81007-8_97

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…We can see from Table 2 that we achieved higher mAP and FPS than all other detection algorithms except YOLOv5s. SDE-YOLO has a higher mAP than Faster-R-CNN [ 22 ], YOLOv3 [ 26 ], YOLOv4 [ 27 ], TE-YOLOF-B3 [ 52 ], and ISE-YOLO [ 53 ], by 19.5, 12.1, 11.3, 4.1, and 10.3 percentage points, respectively. SDE-YOLO has a higher FPS than Faster-R-CNN, YOLOv3, YOLOv4, TE-YOLOF-B3, and ISE-YOLO, by 34.2, 8.9, 7.3, 0.4, and 8.9, respectively.…”

Section: Methodsmentioning

confidence: 99%

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SDE-YOLO: A Novel Method for Blood Cell Detection

Wu,

Gao,

Fang

et al. 2023

Biomimetics

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This paper proposes an improved target detection algorithm, SDE-YOLO, based on the YOLOv5s framework, to address the low detection accuracy, misdetection, and leakage in blood cell detection caused by existing single-stage and two-stage detection algorithms. Initially, the Swin Transformer is integrated into the back-end of the backbone to extract the features in a better way. Then, the 32 × 32 network layer in the path-aggregation network (PANet) is removed to decrease the number of parameters in the network while increasing its accuracy in detecting small targets. Moreover, PANet substitutes traditional convolution with depth-separable convolution to accurately recognize small targets while maintaining a fast speed. Finally, replacing the complete intersection over union (CIOU) loss function with the Euclidean intersection over union (EIOU) loss function can help address the imbalance of positive and negative samples and speed up the convergence rate. The SDE-YOLO algorithm achieves a mAP of 99.5%, 95.3%, and 93.3% on the BCCD blood cell dataset for white blood cells, red blood cells, and platelets, respectively, which is an improvement over other single-stage and two-stage algorithms such as SSD, YOLOv4, and YOLOv5s. The experiment yields excellent results, and the algorithm detects blood cells very well. The SDE-YOLO algorithm also has advantages in accuracy and real-time blood cell detection performance compared to the YOLOv7 and YOLOv8 technologies.

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Section: Methodsmentioning

confidence: 99%

“…The single-stage algorithms are represented by SSD [25] and YOLO [26][27][28], which realize end-to-end detection by considering localization and classification as a regression problem. The single-stage algorithms have fast detection speeds but low accuracy.…”

Section: Introductionmentioning

confidence: 99%

SDE-YOLO: A Novel Method for Blood Cell Detection

Wu,

Gao,

Fang

et al. 2023

Biomimetics

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“…Avramović et al [ 20 ] improved detection accuracy in automotive applications by combining ROI extraction with various YOLO architectures. Fan et al [ 21 ] enhanced detection speed by adopting DenseNet as the backbone network for YOLOv3. Gong et al [ 22 ] modified YOLOv3’s network header to create 152 × 152 feature maps, improving the detection performance of small traffic signs.…”

Section: Related Workmentioning

confidence: 99%

ETSR-YOLO: An improved multi-scale traffic sign detection algorithm based on YOLOv5

Liu,

Zhou,

Zhang

et al. 2023

PLoS ONE

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In the application of driverless technology, current traffic sign recognition methods are susceptible to the influence of ambient light interference, target size changes and complex backgrounds, resulting in reduced recognition accuracy. To address these challenges, this study introduces an optimisation algorithm called ETSR-YOLO, which is based on the YOLOv5s algorithm. First, this study improves the path aggregation network (PANet) of YOLOv5s to enhance multi-scale feature fusion by generating an additional high-resolution feature layer to improve the recognition of YOLOv5s for small-sized objects. Second, the study introduces two improved C3 modules that aim to suppress background noise interference and enhance the feature extraction capabilities of the network. Finally, the study uses the Wise-IoU (WIoU) function in the post-processing stage to improve the learning ability and robustness of the algorithm to different samples. The experimental results show that ETSR-YOLO improves mAP@0.5 by 6.6% on the Tsinghua-Tencent 100K (TT100K) dataset and by 1.9% on the CSUST Chinese Traffic Sign Detection Benchmark 2021 (CCTSDB2021) dataset. In the experiments conducted on the embedded computing platform, ETSR-YOLO demonstrates a short average inference time, thereby affirming its capability to deliver dependable traffic sign detection for intelligent vehicles operating in real-world traffic scenes. The source code and test results of the models used in this study are available at https://github.com/cbrook16/ETSR-YOLO.

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“…The TT100K [ 12 ] dataset, created by Tsinghua University and Tencent in collaboration, was extracted from the Chinese Street View panorama and covers a wide range of lighting and weather conditions, making it more representative of the actual driving environment. Study [ 13 ] used DenseNet instead of ResNet in the backbone network of YOLOv3 and experimentally validated it on the TT100K dataset. The algorithm improves the real-time performance of the detection model, but the accuracy and recall tend to be low when it comes to small targets such as traffic signs, which implies serious misdetection.…”

Section: Introductionmentioning

confidence: 99%

Traffic Sign Recognition Based on the YOLOv3 Algorithm

Gong

Song

et al. 2022

Sensors

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Traffic sign detection is an essential component of an intelligent transportation system, since it provides critical road traffic data for vehicle decision-making and control. To solve the challenges of small traffic signs, inconspicuous characteristics, and low detection accuracy, a traffic sign recognition method based on improved (You Only Look Once v3) YOLOv3 is proposed. The spatial pyramid pooling structure is fused into the YOLOv3 network structure to achieve the fusion of local features and global features, and the fourth feature prediction scale of 152 × 152 size is introduced to make full use of the shallow features in the network to predict small targets. Furthermore, the bounding box regression is more stable when the distance-IoU (DIoU) loss is used, which takes into account the distance between the target and anchor, the overlap rate, and the scale. The Tsinghua–Tencent 100K (TT100K) traffic sign dataset’s 12 anchors are recalculated using the K-means clustering algorithm, while the dataset is balanced and expanded to address the problem of an uneven number of target classes in the TT100K dataset. The algorithm is compared to YOLOv3 and other commonly used target detection algorithms, and the results show that the improved YOLOv3 algorithm achieves a mean average precision (mAP) of 77.3%, which is 8.4% higher than YOLOv3, especially in small target detection, where the mAP is improved by 10.5%, greatly improving the accuracy of the detection network while keeping the real-time performance as high as possible. The detection network’s accuracy is substantially enhanced while keeping the network’s real-time performance as high as possible.

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A Traffic Sign Recognition Method Based on Improved YOLOv3

Cited by 5 publications

References 5 publications

SDE-YOLO: A Novel Method for Blood Cell Detection

SDE-YOLO: A Novel Method for Blood Cell Detection

ETSR-YOLO: An improved multi-scale traffic sign detection algorithm based on YOLOv5

Traffic Sign Recognition Based on the YOLOv3 Algorithm

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