Improved traffic signal light recognition algorithm based on YOLO v3

Yu, Feng; Ming, Zhong; Shifu, Tang; Zheng, Zhonggang

doi:10.1117/12.2634502

Cited by 2 publications

(1 citation statement)

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“…In 2020, Zhu et al [2] proposed the MME-YOLO network structure model, although it is widely utilized in identifying targets in intricate traffic environments, the precision in detecting minute traffic objects within scenes remains insufficient. In 2022, Yu et al [3] improved YOLOv3 model and used to detect the traffic lights, obtaining the good results, however this model cannot be applied to all kinds of target detection in complex traffic scenes. ZHANG et al [4] added BAM to YOLOv5 to increase the amount of attention given to small targets in shallow images, and the method performed well in small person detection; however, the parameter scale was larger.…”

Section: Related Workmentioning

confidence: 99%

Small target detection in aerial images of road scenes based on the improved YOLOv7

Liu,

Zhang,

Shi

et al. 2024

Preprint

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Small target detection in aerial road images is challenging, and the existing detection models are not suitable for small targets with dense, complicated background and occlusion in aerial images. Therefore, a new small target detection model based on YOLOv7 model is used for small target detection in UAV road scenes. We have made four improvements： (i) a new head for detection is added to the original YOLOv7 model to detect tiny-scale targets. (ii) we use the SimAM attention to focus on areas with small-scale targets. (iii) In the downsampling stage, SPD-Conv model is added to replace the original CNN model to improve the capacity to identify small targets of low resolution in aerial road images. (iv) CARAFE model is introduced to enhance feature fusion and reduce information loss. To demonstrate the feasibility of the model, the first set of contrast experiments were first performed on the Drone dataset, the mAP0.5 value is increased by 8.8% compared with that of the original YOLOv7. Then, experiments are also carried out on the VisDrone2019 dataset. In comparison with the original YOLOv7, the mAP0.5 value of the improved YOLOv7 is increased by 7.2%. This model is equally effective for dense and obscured small targets, our detection performance of the improved YOLOv7 is superior to that of recent methods.

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Section: Related Workmentioning

confidence: 99%

Small target detection in aerial images of road scenes based on the improved YOLOv7

Liu,

Zhang,

Shi

et al. 2024

Preprint

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A Multi-Scale Traffic Object Detection Algorithm for Road Scenes Based on Improved YOLOv5

Sun

Zhang

et al. 2023

Electronics

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Object detection in road scenes is a task that has recently become popular and it is also an important part of intelligent transportation systems. Due to the different locations of cameras in the road scenes, the size of the traffic objects captured varies greatly, which imposes a burden on the network optimization. In addition, in some dense traffic scenes, the size of the traffic objects captured is extremely small and it is easy to miss detection and to encounter false detection. In this paper, we propose an improved multi-scale YOLOv5s algorithm based on the YOLOv5s algorithm. In detail, we add a detection head for extremely small objects to the original YOLOv5s model, which significantly improves the accuracy in detecting extremely small traffic objects. A content-aware reassembly of features (CARAFE) module is introduced in the feature fusion part to enhance the feature fusion. A new SPD-Conv CNN Module is introduced instead of the original convolutional structure to enhance the overall computational efficiency of the model. Finally, the normalization-based attention module (NAM) is introduced, allowing the model to focus on more useful information during training and significantly improving detection accuracy. The experimental results demonstrate that compared with the original YOLOv5s algorithm, the detection accuracy of the multi-scale YOLOv5s model proposed in this paper is improved by 7.1% on the constructed diverse traffic scene datasets. The improved multi-scale YOLOv5s algorithm also maintains the highest detection accuracy among the current mainstream object detection algorithms and is superior in accomplishing the task of detecting traffic objects in complex road scenes.

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Improved traffic signal light recognition algorithm based on YOLO v3

Cited by 2 publications

References 0 publications

Small target detection in aerial images of road scenes based on the improved YOLOv7

Small target detection in aerial images of road scenes based on the improved YOLOv7

A Multi-Scale Traffic Object Detection Algorithm for Road Scenes Based on Improved YOLOv5

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