Recognition of ripe litchi in different illumination conditions based on Retinex image enhancement

Abstract: To realize the goal of precise positioning of a picking robot in a natural environment of fruit and vegetables, some problems remain to be solved. The variability of illumination in natural environment is one of the main factors and causes low recognition accuracy and long recognition algorithm running time. In order to meet the effectiveness and real-time requirements of the litchi picking robot visual positioning system in a natural environment, the recognition of ripe litchi in a natural environment was studied. According to the litchi color images in different illumination conditions, to analyze the color features of litchi images a bilateral filtering Retinex image enhancement algorithm was used to highlight the litchi fruit and stem, which was needed to reduce the influence of illumination on litchi image processing and to highlight the recognized target. The color component characteristics in different color spaces of litchi images under different illumination conditions were analyzed to determine the H component rotation in HSI color space to the litchi image after image enhancement processing, which can reduce the influence of uneven illumination under the foundation of maintaining a relative relationship between colors of the original image. According to the bimodal characteristics of the H component grayscale histogram after rotation processing, the Otsu automatic threshold segmentation method for H component image threshold segmentation was chosen to remove the complex background except for the litchi fruit and stem. The fuzzy c-means (FCM) clustering algorithm was then selected to segment the fruit and stem of the litchi image, and due to the characteristics of the artificially given clustering number and low arithmetic speed of the traditional fuzzy c-means clustering algorithm, the fuzzy clustering algorithm was improved. Through the fusion of the bicubic interpolation algorithm and the FCM algorithm, the improved fuzzy c-means clustering algorithm was used in the fuzzy cluster segment of the Cr component in YCbCr color space, in which the recognition of the litchi fruit and stem was realized. One Hundred ten litchi images in different illumination conditions were randomly selected and used for the litchi fruit and stem segmentation experiments based on the research algorithm. The experimental results are that 100 mature litchi images were segmented correctly to fruit and stem, and in the other segmentation results, interference noise such as branches, the sky, and land existed. The recognition accuracy rate of ripe litchi can reach 90.9%. The experiment results show that this algorithm has good stability for litchi image segmentation in different illumination conditions, such as sunshine and front lighting, shade, backlighting, and clouds , mainly for the litchi recognition under the condition of weak light in cloudy days and the shaded fruit, which can well maintain the integrity of the litchi fruit and stem. The research results can provide the theoretical basis and technical support to the effectiveness and real-time for fruit and vegetable picking robot vision positioning systems, and lay the foundation for visual precise positioning.