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학술지 Design of a Scalable and Fast YOLO for Edge-Computing Devices
Cited 13 time in scopus Download 159 time Share share facebook twitter linkedin kakaostory
한병길, 이준구, 임길택, 최두현
Sensors, v.20 no.23, pp.1-15
20ZD1100, 대경권 지역산업 기반 ICT 융합기술 고도화 지원사업, 문기영
With the increase in research cases of the application of a convolutional neural network (CNN)-based object detection technology, studies on the light-weight CNN models that can be performed in real time on the edge-computing devices are also increasing. This paper proposed scalable convolutional blocks that can be easily designed CNN networks of You Only Look Once (YOLO) detector which have the balanced processing speed and accuracy of the target edge-computing devices considering different performances by exchanging the proposed blocks simply. The maximum number of kernels of the convolutional layer was determined through simple but intuitive speed comparison tests for three edge-computing devices to be considered. The scalable convolutional blocks were designed in consideration of the limited maximum number of kernels to detect objects in real time on these edge-computing devices. Three scalable and fast YOLO detectors (SF-YOLO) which designed using the proposed scalable convolutional blocks compared the processing speed and accuracy with several conventional light-weight YOLO detectors on the edge-computing devices. When compared with YOLOv3-tiny, SF-YOLO was seen to be 2 times faster than the previous processing speed but with the same accuracy as YOLOv3-tiny, and also, a 48% improved processing speed than the YOLOv3-tiny-PRN which is the processing speed improvement model. Also, even in the large SF-YOLO model that focuses on the accuracy performance, it achieved a 10% faster processing speed with better accuracy of 40.4% mAP@0.5 in the MS COCO dataset than YOLOv4-tiny model.
Edge-computing, Light-weight, Mobile device, Object detector, Scalable, SF-YOLO
KSP 제안 키워드
Accuracy performance, Comparison tests, Convolution neural network(CNN), Detection technology, Mobile devices, Object detection, Processing speed, Real-Time, edge computing, light-weight, object detector
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