A Fast Identification Method for Fruit Surface DefectBased on Fractal Characters
基于分形特征的水果缺陷快速识别方法

Computer vision and image processing techniques have been found increasingly useful for the fruit automatic quality inspection and defect sorting operation. However, real time fruit surface defect inspection and recognition is still a challenging project due to its complexity. In this paper, a fast approach for box dimension estimation based on a dual pyramid data structure is developed. Utilizing traditional fractal dimension and 4 oriented fractal dimensions as input values, a BP neural network is designed for identifying fruit defect area and stem, calyx concave area. The results of experiment show that the approach is effective for real time defect identification and is accurate. The rate of correct classification is 93% and the executing time of microcomputer for recognition of one undefined blob on the surface of apple is 4~7ms.