A recognition algorithm for occluded tomatoes based on circle regression

To realize the automation of harvesting work, automatic recognition of fruits and vegetables should be realized firstly. In the researches on the recognition of fruits and vegetables, the recognition of occluded fruits and vegetables is a difficult point. This paper presents a recognition algorithm for occluded tomatoes based on circle regression. It mainly bases on the principle that there is a big difference in curvatures between edge points on the edge produced by occlusion and edge points on the edge without occlusion. First, the closed edges of occluded tomatoes were extracted after image segmentation. Second, the curvatures of edge points were computed. Third, edge points with abnormal curvatures were removed. Finally, occluded tomatoes were recognized using circle regression method for edge points with normal curvatures. Moreover, in order to reduce false recognition, edge recognition and circle regression rules were also applied in this study. Test results showed that the correct rate of recognition was larger than 90% for tomatoes with little occlusion, but it was not ideal for tomatoes with moderate and serious occlusion.