Incorporating bidirectional feature pyramid network and lightweight network: a YOLOv5-GBC distracted driving behavior detection model

Du, Yingjie; Liu, Xiaofeng; Yi, Yuwei; Wei, Kun

doi:10.1007/s00521-023-09043-5

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…Wang et al [38] applied YOLOv5s and YOLOv5x networks to their custom CHV dataset, observing that YOLOv5s was 7% less accurate in detecting safety helmets compared to YOLOv5x. Du et al [39] enhanced the YOLOv5 model by incorporating the GhostConv lightweight network and bidirectional feature pyramid modules. This integration significantly improved real-time detection performance, achieving a mAP of 91.8%.…”

Section: Related Workmentioning

confidence: 99%

MEAG-YOLO: A Novel Approach for the Accurate Detection of Personal Protective Equipment in Substations

Zhang,

Mu,

et al. 2024

Applied Sciences

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Timely and accurately detecting personal protective equipment (PPE) usage among workers is essential for substation safety management. However, traditional algorithms encounter difficulties in substations due to issues such as varying target scales, intricate backgrounds, and many model parameters. Therefore, this paper proposes MEAG-YOLO, an enhanced PPE detection model for substations built upon YOLOv8n. First, the model incorporates the Multi-Scale Channel Attention (MSCA) module to improve feature extraction. Second, it newly designs the EC2f structure with one-dimensional convolution to enhance feature fusion efficiency. Additionally, the study optimizes the Path Aggregation Network (PANet) structure to improve feature learning and the fusion of multi-scale targets. Finally, the GhostConv module is integrated to optimize convolution operations and reduce computational complexity. The experimental results show that MEAG-YOLO achieves a 2.4% increase in precision compared to YOLOv8n, with a 7.3% reduction in FLOPs. These findings suggest that MEAG-YOLO is effective in identifying PPE in complex substation scenarios, contributing to the development of smart grid systems.

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Section: Related Workmentioning

confidence: 99%

MEAG-YOLO: A Novel Approach for the Accurate Detection of Personal Protective Equipment in Substations

Zhang,

Mu,

et al. 2024

Applied Sciences

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“…One-stage algorithms are renowned for efficiently identifying object categories and precise locations 26 – 29 . Zongqi’s team 30 , for instance, developed a YOLOv2-based method for detecting foreign objects on power transmission lines.…”

Section: Introductionmentioning

confidence: 99%

GEB-YOLO: a novel algorithm for enhanced and efficient detection of foreign objects in power transmission lines

Zheng,

Liu,

et al. 2024

Sci Rep

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Detecting foreign objects in power transmission lines is essential for mitigating safety risks and maintaining line stability. Practical detection, however, presents challenges including varied target sizes, intricate backgrounds, and large model weights. To address these issues, this study introduces an innovative GEB-YOLO model, which balances detection performance and quantification. Firstly, the algorithm features a lightweight architecture, achieved by merging the GhostConv network with the advanced YOLOv8 model. This integration considerably lowers computational demands and parameters through streamlined linear operations. Secondly, this paper proposes a novel EC2f mechanism, a groundbreaking feature that bolsters the model’s information extraction capabilities. It enhances the relationship between weights and channels via one-dimensional convolution. Lastly, the BiFPN mechanism is employed to improve the model’s processing efficiency for targets of different sizes, utilizing bidirectional connections and swift feature fusion for normalization. Experimental results indicate the model’s superiority over existing models in precision and mAP, showing improvements of 3.7 and 6.8%, respectively. Crucially, the model’s parameters and FLOPs have been reduced by 10.0 and 7.4%, leading to a model that is both lighter and more efficient. These advancements offer invaluable insights for applying laser technology in detecting foreign objects, contributing significantly to both theory and practice.

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Driver Distraction Behavior Detection Framework Based on the DWPose Model, Kalman Filtering, and Multi-Transformer

Shi

2024

IEEE Access

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Incorporating bidirectional feature pyramid network and lightweight network: a YOLOv5-GBC distracted driving behavior detection model

Cited by 3 publications

References 36 publications

MEAG-YOLO: A Novel Approach for the Accurate Detection of Personal Protective Equipment in Substations

MEAG-YOLO: A Novel Approach for the Accurate Detection of Personal Protective Equipment in Substations

GEB-YOLO: a novel algorithm for enhanced and efficient detection of foreign objects in power transmission lines

Driver Distraction Behavior Detection Framework Based on the DWPose Model, Kalman Filtering, and Multi-Transformer

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