Potentials of Machine Learning for Data analysis in IoT: A Detailed Survey

Vasoya, Shreya; Patel, Nishi; Ramoliya, Dipak; Patel, Khushi

doi:10.1109/iciss49785.2020.9316074

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Deep Learning algorithms can obtain a great degree of perfection in front of large datasets [15], so we collect numbers of meter data from the transmission lines of China Southern Power Grid. The data is manually cleaned by multiple professional, marked the rectangular frame coordinate value of the existing object, and labeled into six categories, including five categories: thermometer square, thermometer circle, lightning arrester, oil level gauge and SF6 meter.…”

Section: Datasetmentioning

confidence: 99%

A Lightweight Meter Detection Method Based on Yolov5

Shao

Лонг

et al. 2022

Modern Management Based on Big Data III

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With the deepening of industrial automation, a large number of edge intelligent devices are deployed in industrial meter detection. In view of the limited computing and storage capacity of these embedded devices, we propose a lightweight meter detection method. Our proposed method is based on the widely used Yolov5, the depthwise separable convolution and squeeze and excitation channel attention module are used to simplify the backbone and head of the network, and further prune the filters of convolution layers via geometric median. Finally, model parameters and floating-point operations are reduced to 0.250M and 0.687G on the premise of ensuring the effect of the meter detection.

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Section: Datasetmentioning

confidence: 99%