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As an infrastructure of biochemical laboratories, EP tube label plays a significant role in information extraction to meet the limitations of computing power in offline devices and solve the problem that the EP tube label cannot be accurately identified before identification because the label belongs to multi-angle random placement. This paper proposes a light-weight neural network YOLOv4-tiny-ECA to position tubes and a tilt correction method based on Hough transform. First, the EP tube rack is roughly positioned based on the diffuse filling algorithm combined with digital morphological corrosion, and then the EP tubes in the rack are precisely positioned using the light-weight YOLO target detection algorithm combined with the attention mechanism. Next, the baseline is added to the label as the basis for determining the tilt angle. For the valid target, the baseline is extracted using the Hough transform and the tilt angle is calculated by vector fork multiplication. Finally, baseline is removed using image processing algorithm for better recognition results. Our results show that the light-weight YOLO algorithm reduces the network parameters by 56% and computation by 55% while keeping the accuracy rate largely unchanged, the offline positioning tilt correction method can achieve 98.8% accuracy and 0.076 s processing speed for a single test tube on average, which meets the real-time requirement. [ABSTRACT FROM AUTHOR]

Traditional Hough circle detection algorithm usually determines the center and radius of a circle by mapping points in cartesian coordinate system to polar coordinate system. Since it accumulates in the three-dimensional space, it requires more calculation consumption. In this paper, we solve the problem of high time complexity of Hough algorithm in judging circle radius and circle center from two aspects of circle angle and circle radius according to the geometric features of quasi-circles. A large number of experiments show that, compared with the traditional algorithm, this algorithm can not only identify quasi-circles, but also improve the detection success rate of circles by about 10%, with efficient running speed, and obtain good experimental results in the detection of craters. [ABSTRACT FROM AUTHOR]

Traditional Hough circle detection algorithm usually determines the center and radius of a circle by mapping points in cartesian coordinate system to polar coordinate system. Since it accumulates in the three-dimensional space, it requires more calculation consumption. In this paper, we solve the problem of high time complexity of Hough algorithm in judging circle radius and circle center from two aspects of circle angle and circle radius according to the geometric features of quasi-circles. A large number of experiments show that, compared with the traditional algorithm, this algorithm can not only identify quasi-circles, but also improve the detection success rate of circles by about 10%, with efficient running speed, and obtain good experimental results in the detection of craters. [ABSTRACT FROM AUTHOR]