Design of a Scalable and Fast YOLO for Edge-Computing Devices

Han, Byung-Gil; Lee, Joon-Goo; Lim, Kil-Taek; Choi, Doo−Hyun

doi:10.3390/s20236779

Cited by 30 publications

(16 citation statements)

References 23 publications

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“…They derive processing times of just below 10 ms, which reaches service latency levels that are suitable for real-time object detection. Indeed, the interest for improvement and implementation for YOLO at the network edge is continuously attracting research interest [41][42][43] to improve upon the continuously developed YOLO, including hardware implementations [44]. Comparing these optimized approaches to our evaluation base don CPU processing alone is limited, as mostly, GPU or specialized hardware is employed for this type of task.…”

Section: Discussionmentioning

confidence: 99%

You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks

2021

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With increasing numbers of computer vision and object detection application scenarios, those requiring ultra-low service latency times have become increasingly prominent; e.g., those for autonomous and connected vehicles or smart city applications. The incorporation of machine learning through the applications of trained models in these scenarios can pose a computational challenge. The softwarization of networks provides opportunities to incorporate computing into the network, increasing flexibility by distributing workloads through offloading from client and edge nodes over in-network nodes to servers. In this article, we present an example for splitting the inference component of the YOLOv2 trained machine learning model between client, network, and service side processing to reduce the overall service latency. Assuming a client has 20% of the server computational resources, we observe a more than 12-fold reduction of service latency when incorporating our service split compared to on-client processing and and an increase in speed of more than 25% compared to performing everything on the server. Our approach is not only applicable to object detection, but can also be applied in a broad variety of machine learning-based applications and services.

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Section: Discussionmentioning

confidence: 99%

You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks

2021

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“…Although YOLOv5 has yet to present novel model architecture enhancements to the family of YOLO models, it announces a new PyTorch training and deployment framework which increases the state-of-the-art for object detectors. 24 Figure 10 shows YOLO v5 model structure.…”

Section: Yolo V5mentioning

confidence: 99%

A robust multiclass 3D object recognition based on modern YOLO deep learning algorithms

Francies

Ata

Mohamed

2021

Concurrency and Computation

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A multiclass 3D object recognition has perceived a numerous evolution with respect to both accuracy and speed. This study introduces the implementation of modern YOLO algorithms (YOLOv3, YOLOv4, and YOLOv5) for multiclass 3D object detection and recognition. All YOLO algorithms have been tested according to a very large scaled dataset (Pascal VOC dataset). Performance evaluation has targeted the calculation of the following metrics; mAP (mean average precision), recall, F1-score, IOU (intersection over union), and the running time. Experimental results demonstrate that the YOLOv3 has targeted mAP of 77%, IOU of 0.41 and the total running time was almost 8 h. Moreover, in YOLOv4, it has targeted mAP of 55%, IOU of 0.035 and the total running time nearly 7 h. In addition, YOLOv5 has established the mAP of 48%, IOU of 0.045, and the total running time was about 3 h. Finally, a modified version of YOLOv5 has been proposed in the state-of-the-art of optimizing its hyperparameters and layering system. Accordingly, the mAP scored about 55% with 3 h running time. The final conclusions of this study have demonstrated that YOLOv3 has scored the highest recognition accuracy, however, the proposed modified YOLOv5 has scored the least processing time.

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“…An improved YOLOv3-Tiny model is used to detect kiwi fruit [23]. Han et al modified the YOVOV4-Tiny model to perform detection more quickly and more accurately on devices with limited computational capabilities [24]. To the best of our knowledge, there are still few works about the application of edge computing in forest protection, which is critical to detect the PWD infected trees in time.…”

Section: Introductionmentioning

confidence: 99%

A Remote Sensing and Airborne Edge-Computing Based Detection System for Pine Wilt Disease

Liu

Shen³

et al. 2021

IEEE Access

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The pine wilt disease (PWD) is one of the most dangerous and destructive diseases to coniferous forests. The rapid spread trend and strong destruction directly threaten the security of forests. The complex spread pattern and the hard labor process of diagnosis call for an effective way to detect the infected areas. In this paper, an airborne edge-computing and lightweight deep learning based system are designed for PWD detection by using imagery sensors. Unmanned aerial vehicle (UAV) is firstly utilized to realize a large-scale coverage of forests, which can substantially reduce the hard labor. Except for infected trees, a large number of irrelevant images are also acquired by the UAV, which will overload the burden of process and transmission. Then a lightweight improved YOLOv4-Tiny based method (named as YOLOv4-Tiny-3Layers) is proposed to filter these uninterested images by leveraging the computation capability of edge computing, which can realize a fast coarse-grained detection with a low missing rate. Finally, all the remaining images are transmitted to the ground workstation for the final fine-grained detection. Experimental results show that the proposed system can implement a fast detection with superior performance as compared to other methods, which helps to detect the infected pine trees in a quick manner.INDEX TERMS pine wilt disease; remote sensing; airborne edge computing; lightweight deep learning; two-stage detection

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Design of a Scalable and Fast YOLO for Edge-Computing Devices

Cited by 30 publications

References 23 publications

You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks

You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks

A robust multiclass 3D object recognition based on modern YOLO deep learning algorithms

A Remote Sensing and Airborne Edge-Computing Based Detection System for Pine Wilt Disease

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