An Efficient Deep Learning Framework for Distracted Driver Detection

Sajid, Faiqa; Javed, Abdul Rehman; Basharat, Asma; Kryvinska, Natalia; Afzal, A. L.; Rizwan, Muhammad

doi:10.1109/access.2021.3138137

Cited by 44 publications

(26 citation statements)

References 30 publications

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“…YOLOX(M) has the highest FLOPs [32], even surpassing the SSD, while mAP does not perform well enough, scoring only 66.60%, and EFFICIENTDET(D0) [33] has the worst FLOPs among the three, with only 1/3 of the FLOPs of the GhostNet-SSD. The CENTERNET algorithm, which has the best overall performance, also performs poorly compared to the GhostNet-SSD, with an mAP value 5.32% lower than GhostNet-SSD, and a running speed is only 57.08%.…”

Section: E Results Analysismentioning

confidence: 98%

Research on Pet Recognition Algorithm With Dual Attention GhostNet-SSD and Edge Devices

Hou

Meng

2022

IEEE Access

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The ability to identify pets via smart home technology is crucial for managing pet health. However, the present popular object detection method model is too large, support for edge devices is insufficient, and performance is subpar for small object identification in the traditional algorithm, such as SSD. In this study, we propose an improved SSD object detection network algorithm based on a GhostNet network with a dual attention mechanism. A ghost module is introduced to create a lightweight ghost network based on the SSD network. The ghost module is combined with the ECA attention mechanism to dynamically allocate parameters to the target and alter the detection region weights to enhance the model performance. In order to reinforce the model and increase the precision of the small target, the CBAM module was also introduced. It assigns major weights to the output target regions of the large-scale feature layer. According to the experimental findings, the CBAM-GhostNet-SSD network significantly reduces the number of backbone parameters and the calculation amount is reduced by 98.23% when compared to the classic SSD. This lays the groundwork for the self-developed algorithm's successful deployment of edge devices. The predicted rate synchronization increased by 3.1 times, also mAP was 14.5% higher than SSD. The lightweight model quantitative transformation utilized in edge equipment can accurately evaluate the target region and realize dynamic detection. This has a certain guiding significance for the subsequent detection of household pet targets.INDEX TERMS deep learning, object detection, ghostnet, attention modules, edge devices.

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Section: E Results Analysismentioning

confidence: 98%

Research on Pet Recognition Algorithm With Dual Attention GhostNet-SSD and Edge Devices

Hou

Meng

2022

IEEE Access

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“…To verify the advanced nature of the detection model in this paper, its AP, mAP, parameter quantity and model complexity were compared with those of the EfficientDet-D0, EfficientDet-D1, EfficientDet-D2, EfficientDet-D3 algorithms [27], and the optimal model of the YOLO series. In addition, since the DETR [28] requires 10 to 20 times more training epoch than modern mainstream detectors to converge, in order to get the performance of the converged DETR, epochs are added to the Table 2 for comparison with other DETR-based models [29], [30], [31].…”

Section: Results and Discussion 1) Comparison With Other Algorithmsmentioning

confidence: 99%

PV-YOLO: Lightweight YOLO for Photovoltaic Panel Fault Detection

et al. 2023

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The rapid development of the photovoltaic industry in recent years has made the efficient and accurate completion of photovoltaic operation and maintenance a major focus in recent studies. The key to photovoltaic operation and maintenance is the accurate multifault identification of photovoltaic panel images collected using drones. In this paper, PV-YOLO is proposed to replace YOLOX's backbone network, CSPDarknet53, with a transformer-based PVTv2 network to obtain local connections between images and feature maps to extract more edge-detail features of similar faults. The CBAM attention mechanism is added to enhance the effective features and improve the detection accuracy of small objects. The label assignment mechanism is optimized, and the SIoU loss functionis used to improve the uneven distribution of samples and accelerate network convergence. Experiments on the dataset prove that this method is superior to the existing technology, as the highest mAP value is 92.56%. This value is 10.46% higher than that of YOLOX, and the mAP is optimal under the same parameter magnitude,proving the model's effectiveness.Moreover, mAP is increased by over 10%, especially for small targets. In this paper, we implemented a lightweight design for the model, and proposes four models of different sizes to be-sized models that are suitable for different detection scenarios.

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“…[107] designed a typical driver activities recognition system based on a vision-based sensor leveraging deep convolutional neural networks (CNN). The sensor could identify seven common driving activities by detecting the driver's body posture; similar work can also be found in [108][109][110][111]. This approach typically achieves almost 99% accuracy in detecting the limited driver activity.…”

Section: A Driver Distraction Detectionmentioning

confidence: 99%

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

Lou

Xing

et al. 2022

IEEE Trans. Intell. Veh.

100

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Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design of the automated vehicle, whereas the digitization of the human driver, who plays an important role in driving, is largely ignored. Furthermore, previous driver-related tasks are limited to specific scenarios and have limited applicability. Thus, a novel concept of a driver digital twin (DDT) is proposed in this study to bridge the gap between existing automated driving systems and fully digitized ones and aid in the development of a complete driving human cyber-physical system (H-CPS). This concept is essential for constructing a harmonious human-centric intelligent driving system that considers the proactivity and sensitivity of the human driver. The primary characteristics of the DDT include multimodal state fusion, personalized modeling, and time variance. Compared with the original DT, the proposed DDT emphasizes on internal personality and capability with respect to the external physiological-level state. This study systematically illustrates the DDT and outlines its key enabling aspects. The related technologies are comprehensively reviewed and discussed with a view to improving them by leveraging the DDT. In addition, the potential applications and unsettled challenges are considered. This study aims to provide fundamental theoretical support to researchers in determining the future scope of the DDT system.

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An Efficient Deep Learning Framework for Distracted Driver Detection

Cited by 44 publications

References 30 publications

Research on Pet Recognition Algorithm With Dual Attention GhostNet-SSD and Edge Devices

Research on Pet Recognition Algorithm With Dual Attention GhostNet-SSD and Edge Devices

PV-YOLO: Lightweight YOLO for Photovoltaic Panel Fault Detection

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

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