Prickly ash appearance quality detection based on computer vision and its implementation in MATLAB

A new approach to prickly ash appearance quality detection was presented based on the computer vision technology tomeet the requirements of speediness, automation, high accuracy, and avoid using traditional manual method which encounters with some problems such as bad repeatability, low efficiency and high random. Images with the method of edge detection by Canny detector have some granules whose edge information was not extracted continuously, so a new algorithm named as second filling was presented to recognize the granule more accurately. Research indicates that each kind of shape feature such as solidity, eccentricity and area has different values and can be used as the separating parameters by comparing each feature respectively for the four ingredients, which are the seed, the fruit coat closing, the peduncle and the seedcase. Experiment and data analysis illustrate that, with the image resolution being 600dpi, the seed can be separated firstly if only solidity and area are 0.95 and 7000 pixels respectively, then the peduncle can be separated with the eccentricity being 0.9, the fruit coat closing and seedcase can be separated finally if the solidity and eccentricity are 0.8964 and 0.5072 respectively. A prickly ash appearance quality detection software was developed correspondingly in the platform of MATLAB R2006a, and indexes such as degree of uniformity, rate of seed，rate of fruit coat closing and rate of peduncle were detected by the system. The results show that the identifying accuracy is 100% for both the seed and the peduncle, 89% and 96.8% for the fruit coat closing and the seedcase respectively. This approach is efficient and credible, so it can help to improve prickly ash quality detection with computer vision theoretically and technologically.