Traffic Sign Detection and Recognition in Multiimages Using a Fusion Model With YOLO and VGG Network

Yu, Jing; Ye, Xiaojun; Tu, Qiang

doi:10.1109/tits.2022.3170354

Cited by 41 publications

(13 citation statements)

References 23 publications

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“…Chen et al [19] added a receptive field module and refined head to the neck of YOLOv4 and designed a TSR-SA model for small target recognition. Yu et al [20] proposed a fusion network model based on YOLOv3 and VGG19, which utilized the relation between image sequences to improve traffic sign recognition accuracy. The single-stage recognition method has made great progress in recognition speed, but it has strong data dependence, which may lead to generalization ability reduction in complex driving scenarios.…”

Section: Related Workmentioning

confidence: 99%

AIF-YOLO: small traffic sign recognition based on YOLOv7

Luo,

Chen,

Liao

2024

Preprint

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Traffic sign recognition is an essential part of intelligent transportation systems and autonomous driving. However, due to the small size of traffic signs, there are a few problems in traffic sign recognition, such as missed recognition, false recognition, and so on. Therefore, we choose YOLOv7 as the baseline and propose an anti-interference feature enhancement YOLO, abbreviated as AIF-YOLO. Firstly, to enhance the network’s feature extraction capability, we add an adaptive feature extraction module (AFEM) structure to adaptive adjust the size of receptive field and refine features. Secondly, we introduce a contextual transformer into the feature extraction network of YOLOv7, resulting in the contextual transformer efficient layer aggregation network (CoT-ELAN) module. This improvement enables the model to capture the relation between traffic signs and environment and thus separate the interference signs. Furthermore, Wise-IoU loss was adopted to optimize network training, effectively addressing the poor quality label issue in the traffic sign dataset. Experiments were tested on Tsinghua-Tencent 100k dataset(TT100K), and the results show that our improved model performed better than the baseline YOLOv7, and other popular algorithms on precision, recall, and mAP@0.5.

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Section: Related Workmentioning

confidence: 99%

AIF-YOLO: small traffic sign recognition based on YOLOv7

Luo,

Chen,

Liao

2024

Preprint

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“…Based on the above, many researchers have improved their feature extraction capabilities by improving the feature extraction module of the network itself [12][13][14][15], among which the optimization of lightweight networks [16][17][18][19][20] is a good remedy. Lightweight networks such as CSPNet [18], MobileNet [19] and VGG [21] accomplish traffic sign tasks with their simple and efficient backbones, but simple use of them will affect the detection accuracy of small-sized objects due to their limited model width and depth.…”

Section: Introductionmentioning

confidence: 99%

“…Liang et al [26] achieved optimization of dim images through a Recurrent Exposure Generation (REG) module and seamlessly connected it with Multi-Exposure Detection (MED) to suppress nonuniform illumination and noise problems. Yu et al [16] proposed a fusion model based on YOLOv3 and VGG19 networks to achieve accurate detection of traffic signs underexposure and dimness using relationships in multiple images. Yuan et al [27] proposed a color angle model to provide color differentiation information as a way to improve the detection of traffic signs after color changes.…”

Section: Introductionmentioning

confidence: 99%

Vehicle-Mounted Adaptive Traffic Sign Detector for Small-Sized Signs in Multiple Working Conditions

Wang,

Chen,

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Traffic sign detection is of great significance to the development of the Intelligent Transportation System (ITS) as a database for environmental awareness. The main challenges of existing traffic sign detection method are inaccurate small object detection, difficult mobile deployment, and complex working environment. Based on these, a vehicle-mounted adaptive traffic sign detector (VATSD) for small-sized signs in multiple working conditions is proposed in this paper. First, the Backbone of the detector is optimized. A feature tight fusion structure is designed to constitute a new feature extraction module, DCSP, which improves the feature extraction capability and the detection accuracy of small objects with negligible additional parameters. Second, an image enhancement network IENet with an adaptive joint filtering strategy is proposed. The IENet enables the dynamic selection of filters and thus adaptively optimizes low-quality images under multiple conditions to improve the accuracy of subsequent detection tasks. The proposed method has experimented on three traffic sign datasets and the detection accuracy increased by up to 7.6% compared to the original. The proposed detector demonstrates superiority over other state-ofthe-art (SOTA) methods in terms of small object detection accuracy, detection speed, and environmental adaptability. Further, we deployed VATSD to Jetson Xavier NX and achieved a detection speed of 21.6 FPS, meeting real-time requirements.

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“…Before YOLOv5, YOLOv3, and YOLOv4 were widely used for traffic sign detection and related tasks. Yu [27]et al proposed a novel model that fused YOLOv3 and VGG19 networks to detect and recognize traffic signs in video sequences using relationships in multiple images. Based on YOLOv4-Tiny, Wang et al [28] proposed the use of a large-scale feature map optimization strategy to enhance the representation of feature information for small targets and improve detection accuracy at long distances, using an improved NMS algorithm to improve the accuracy and recall of targets.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Traffic Sign Recognition Model Based on Improved YOLOv5

Yang

Sun

Zhu

et al. 2023

Preprint

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Traffic sign detection and recognition is a key technology for achieving automatic vehicle driving and maintaining road safety. The paper proposes a lightweight recognition algorithm based on YOLOv5 to address the problems of large model size, complex computation, low detection accuracy and high computational cost of existing traffic sign recognition algorithms. The algorithm is based on YOLOv5, replacing the convolutional structure in the original YOLOv5 neck network with Ghost Module and C3Ghost Module, thus reducing the redundant features in the feature fusion process, lowering the computational cost and the number of parameters; improving the PAN structure of the network and introducing the hybrid attention mechanism module CBAM, which ignores the unimportant information and capturing key information in traffic signs; adding cross-layer connections to the feature pyramid network shortens the path of information transfer, fuses more features, and improves the network feature recognition accuracy. In addition, the EIoU_Loss function is used as the bounding box regression loss function to improve the localization accuracy of the algorithm. The performance of the improved algorithm is also verified on the Chinese traffic sign dataset. The experimental results show that the improved algorithm improves the detection accuracy by 1.2% over the existing YOLOv5 algorithm, mAP@0.5 and mAP@0.5:0.95 by 1.5% and 3.4% respectively, and the overall number of parameters and computational cost of the model are reduced by 14.5% and 16%. The proposed algorithm has enhanced recognition capability for targets in multiple environments and can meet the requirements for real-time traffic sign recognition.

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Traffic Sign Detection and Recognition in Multiimages Using a Fusion Model With YOLO and VGG Network

Cited by 41 publications

References 23 publications

AIF-YOLO: small traffic sign recognition based on YOLOv7

AIF-YOLO: small traffic sign recognition based on YOLOv7

Vehicle-Mounted Adaptive Traffic Sign Detector for Small-Sized Signs in Multiple Working Conditions

A Lightweight Traffic Sign Recognition Model Based on Improved YOLOv5

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