2022
DOI: 10.1007/s11042-022-13226-y
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Reading detection of needle-type instrument in a noisy environment using computer vision-based algorithms

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Cited by 3 publications
(2 citation statements)
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“…High quality meter images can facilitate the extraction of the basic information needed for the reading recognition process, and image quality enhancement is necessary after obtaining the meter images. Image processing-based methods for meter image quality enhancement include methods such as image greyscaling to reduce computational effort [30], threshold segmentation to obtain target regions [31], and histogram equalisation to improve image contrast [32]. For the difficulty in obtaining real datasets, researchers have generated virtual meteration data in large quantities by means of deep learning [33,34].…”
Section: Related Workmentioning
confidence: 99%
“…High quality meter images can facilitate the extraction of the basic information needed for the reading recognition process, and image quality enhancement is necessary after obtaining the meter images. Image processing-based methods for meter image quality enhancement include methods such as image greyscaling to reduce computational effort [30], threshold segmentation to obtain target regions [31], and histogram equalisation to improve image contrast [32]. For the difficulty in obtaining real datasets, researchers have generated virtual meteration data in large quantities by means of deep learning [33,34].…”
Section: Related Workmentioning
confidence: 99%
“…In this paper, a method of pointer detection and determination of the direction of the needle tip is proposed, and the pointer detection is completed. The pointer of the pressure gauge has the characteristics of a large pointer root and small needle tip, and there is a large color difference between the color of the pointer and the background color of the meter panel [28]. Secondly, a half-pointer detection method is used to improve the accuracy and speed of pointer extraction [29].…”
Section: Pointer Reading Identificationmentioning
confidence: 99%