Abstract
An image recognition technique is proposed for determining optimal neck levels for standard metal gauges, in the process of validating pipe provers. A camera-level follow-up control system was designed to achieve automated tracking of fluid levels by a camera, thereby preventing errors from inclined viewing angles. An orange background plate was placed behind the tube to reduce background interference, and highlight scale numbers/lines and concave meniscus. A segmentation algorithm, based on edge detection and K-means clustering, was used to segment indicator tubes and scales in the acquired images. The concave meniscus reconstruction algorithm and curve-fitting algorithm were proposed to better identify the lowest point of the meniscus. A characteristic edge detection model was used to identify centimeter-scale lines corresponding to the meniscus. A binary tree multiclass support vector machine (MCSVM) classifier was then used to identify scale numbers corresponding to scale lines and determine the optimal neck level for standard metal gauges. Experimental results showed that measurement errors were within ±0.1 mm compared to a ground truth acquired manually using Vernier calipers. The recognition time, including follow-up control, was less than 10 s, which is much lower than the switching time required between measuring individual tanks. This automated measurement approach for gauge neck levels can effectively reduce measurement times, decrease manmade errors in liquid level readings, and improve the efficiency of pipe prover validation.
Highlights
The metering of oil flow is critical for crude oil production and trade
This study proposes a new level measurement technique, based on image recognition, to improve improve the accuracy and speed of liquid height readings
After the camera is moved to the required position, the focal length is adjusted and another image is acquired for accurate liquid level measurements, thereby achieving automated tracking
Summary
The metering of oil flow is critical for crude oil production and trade. As such, flow meters must be regularly verified to ensure accuracy. This approach did not consider measurement errors caused by differences in camera viewing angles, which reduced its accuracy and are used to measure thethe level [3], which prevents automated calculation of the water volume.system. Identification technique was used to readof scale and was recognition, the center of gravity of the concave meniscus the numbers liquid tube is line usedpositioning as the reading achieved using horizontal projection statistics This approach did not consider measurement point in the liquid level, and the reading method is based on the level of the liquid level at two errors caused by differences in accuracy camera viewing reduced accuracy and did standard points, and the is lowerangles, than ±which.
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