The release of new corn products must be tested, and the measurement of the characteristics of the test items for the ear use manual measurement methods. Manual measurement methods are subject to subjective errors, inconsistent standards, and low test efficiency. Automatic corn seeding machine based on machine vision technology to achieve automatic calculation of corn seed traits will greatly improve the efficiency of new varieties, provide fine data for the screening of new varieties. In machine vision-based automatic ear test system, image calibration algorithm is the key link, which directly affects the accuracy of automatic test data. In particular, the corn ear test is mostly performed outdoors, and the light environment has a certain influence on the image calibration algorithm.
Currently, image calibration algorithms mainly include traditional image calibration methods, image self-calibration methods, and active vision-based image calibration methods. The traditional image calibration method has high calibration accuracy and is suitable for conditional calibration requiring high precision and infrequent changes of camera internal and external parameters. The two-step method is a typical traditional image calibration method.
An image calibration algorithm that is insensitive to the light environment, first removes the error data (noise, disconnection, etc.) from the collected image data based on Bernoulli's Law of Large Numbers, and then reduces the random error by obtaining the average value of the data and improves the algorithm. Accuracy. The test results of automatic corn tester show that the algorithm is insensitive to the light environment and can meet the needs of outdoor automatic ear test system.
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