An Improved Light-Weight Traffic Sign Recognition Algorithm Based on YOLOv4-Tiny

Wang, Lanmei; Zhou, Kun; Chu, Anliang; Wang, Guibao; Wang, Lizhe

doi:10.1109/access.2021.3109798

Cited by 79 publications

(55 citation statements)

References 27 publications

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“…Yolov4-Tiny object detection network is used for object detection experiment, and the results are shown in Figure 2. The structural diagram of this model is shown in Figure 3 below [14,15].…”

Section: Yolov4-tinymentioning

confidence: 99%

RGB-D Visual SLAM Based on Yolov4-Tiny in Indoor Dynamic Environment

Chang

Sun³

et al. 2022

Micromachines

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For a SLAM system operating in a dynamic indoor environment, its position estimation accuracy and visual odometer stability could be reduced because the system can be easily affected by moving obstacles. In this paper, a visual SLAM algorithm based on the Yolov4-Tiny network is proposed. Meanwhile, a dynamic feature point elimination strategy based on the traditional ORBSLAM is proposed. Besides this, to obtain semantic information, object detection is carried out when the feature points of the image are extracted. In addition, the epipolar geometry algorithm and the LK optical flow method are employed to detect dynamic objects. The dynamic feature points are removed in the tracking thread, and only the static feature points are used to estimate the position of the camera. The proposed method is evaluated on the TUM dataset. The experimental results show that, compared with ORB-SLAM2, our algorithm improves the camera position estimation accuracy by 93.35% in a highly dynamic environment. Additionally, the average time needed by our algorithm to process an image frame in the tracking thread is 21.49 ms, achieving real-time performance.

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Section: Yolov4-tinymentioning

confidence: 99%

RGB-D Visual SLAM Based on Yolov4-Tiny in Indoor Dynamic Environment

Chang

Sun³

et al. 2022

Micromachines

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“…Subsequently, a series of lightweight networks optimising convolutional computation performed had been proposed [33–35]. In object detection, many lightweight detection networks, such as YOLOv4 [20], YOLOv4‐tiny [36], and YOLOv5 [37], have emerged in detection to overcome the problem of time‐consuming two‐stage algorithms. The goal of these lightweight networks is to achieve balanced accuracy, while reducing the number of model parameters and increasing the inference speed of the model.…”

Section: Related Workmentioning

confidence: 99%

Lightweight and Deep Appearance Embedding for Multiple Object Tracking

Zheng

et al. 2022

IET Computer Vision

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The main challenge of Multiple Object Tracking (MOT) is that there is great uncertainty in data association when using the tracked predicted values and tracked trajectories. Meanwhile, the MOT is complex and time‐consuming. When equipment resources are limited or real‐time requirement is high, its application is very limited. Therefore, we propose a Lightweight Deep Appearance Embedding (LDAE) to assist the association of trajectories. Firstly, in addition to motion information in data association, we also introduce more discriminative appearance features to participate in the affinity measure to effectively distinguish similar targets. Secondly, according to the idea of feature mapping, we design a lightweight deep appearance embedding module. It can help extract appearance features with less computation. Finally, we propose a simulated occlusion strategy for the training of the LDAE, which helps improve the ability to recognise different targets in dense scenes. The LDAE dramatically reduces the computational cost and improves the accuracy of data association. Extensive experiments are conducted on the MOT datasets (MOT16, MOT17 and MOT20), which prove that the LDAE outperforms several state‐of‐the‐art trackers in the tracking accuracy and anti‐occlusion performance. Furthermore, we apply the LDAE to escalators, which can achieve fast and stable tracking effect.

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“…However, computer vision requires a general CNN structure [6] to construct a more lightweight and high-precision architecture. YOLO series algorithms can achieve a real-time detection rate while maintaining factual accuracy [7].…”

Section: Introductionmentioning

confidence: 99%

Faster Light Detection Algorithm of Traffic Signs Based on YOLOv5s-A2

Kuerban

Chen

et al. 2023

IEEE Access

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Traffic sign recognition systems have been applied to advanced driving assistance and automatic driving systems to help drivers obtain important road information accurately. The current mainstream detection methods have high accuracy in this task, but the number of model parameters is large, and the detection speed is slow. Based on YOLOv5s as the basic framework, this paper proposes YOLOv5S-A2, which can improve the detection speed and reduce the model size at the cost of reducing the detection accuracy. Firstly, a data augmentation strategy is proposed by combining various operations to alleviate the problem of unbalanced class instances. Secondly, we proposed a path aggregation module of Feature Pyramid Network (FPN) to make new horizontal connections. It can enhance multi-scale feature representation capability and compensate for the loss of feature information. Thirdly, an attention detection head module is proposed to solve the aliasing effect in cross-scale fusion and enhance the representation of predictive features. Experiments on Tsinghua-Tencent 100K dataset (TT100K) show that our method can achieve more remarkable performance improvement and faster inference speed than other advanced technologies. Our method achieves 87.3% mean average precision (mAP) surpassing the original model's 7.9%, and the frames per second (FPS) value is maintained at 87.7. To show generality, we tested it on the German Traffic Sign Detection Benchmark (GTSDB) without tuning and obtained an average precision of 94.1%, and the FPS value is maintained at about 105.3. In addition, the number of YOLOv5s-A2 parameters is about 7.9 M.

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An Improved Light-Weight Traffic Sign Recognition Algorithm Based on YOLOv4-Tiny

Cited by 79 publications

References 27 publications

RGB-D Visual SLAM Based on Yolov4-Tiny in Indoor Dynamic Environment

RGB-D Visual SLAM Based on Yolov4-Tiny in Indoor Dynamic Environment

Lightweight and Deep Appearance Embedding for Multiple Object Tracking

Faster Light Detection Algorithm of Traffic Signs Based on YOLOv5s-A2

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