Image matching algorithm of defects on navel orange surface based on compressed sensing

The surface defect of navel orange is one of the significant factors that affects its price. At present, most of surface defect detection algorithms for navel orange have disadvantages of slow speed, massive calculation and low efficiency, making it difficult to meet the needs of automated detection. This article proposes an improved image matching method on navel orange surface defect detection which combines wavelet transform (WT) and speeded up robust features (SURF) based on compressed sensing (CS). Firstly, do some pre-treatment on the navel orange images such as de-noising, compression and so on, then decompose the image by wavelet transform based on compressed sensing technology, and obtain the low frequency sub-image and extract SURF features of the image, next compare the extracted SURF feature with feature library, search for the maximum matching value of the similarity measurement values, and output the recognition results. The algorithm ensures better recognition accuracy and efficiency, and achieves rapid identification of navel orange defects.