The key problem for picking robots is to locate the picking points of fruit. A method based on the moment of inertia and symmetry of apples is proposed in this paper to locate the picking points of apples. Image pre-processing procedures, which are crucial to improving the accuracy of the location, were carried out to remove noise and smooth the edges of apples. The moment of inertia method has the disadvantage of high computational complexity, which should be solved, so convex hull was used to improve this problem. To verify the validity of this algorithm, a test was conducted using four types of apple images containing 107 apple targets. These images were single and unblocked apple images, single and blocked apple images, images containing adjacent apples, and apples in panoramas. The root mean square error values of these four types of apple images were 6.3, 15.0, 21.6 and 18.4, respectively, and the average location errors were 4.9°, 10.2°, 16.3° and 13.8°, respectively. Furthermore, the improved algorithm was effective in terms of average runtime, with 3.7 ms and 9.2 ms for single and unblocked and single and blocked apple images, respectively. For the other two types of apple images, the runtime was determined by the number of apples and blocked apples contained in the images. The results showed that the improved algorithm could extract symmetry axes and locate the picking points of apples more efficiently. In conclusion, the improved algorithm is feasible for extracting symmetry axes and locating the picking points of apples. Additional key words: picking robot; fruit picking point; symmetry axes extraction; moment of inertia; convex hull
Accurate segmentation of apple targets is one of the most important problems to be solved in the vision system of apple picking robots. This work aimed to solve the difficulties that background targets often bring to foreground targets segmentation, by fusing the visual attention mechanism and the growth rule of seed points. Background targets could be eliminated by extracting the ROI (region of interest) of apple targets; the ROI was roughly segmented on the HSV color space, and then each of the pixels was used as a seed growing point. The growth rule of the seed points was adopted to obtain the whole area of apple targets from seed growing points. The proposed method was tested with 20 images captured in a natural scene, including 54 foreground apple targets and approximately 84 background apple targets. Experimental results showed that the proposed method can remove background targets and focus on foreground targets, while the k-means algorithm and the chromatic aberration algorithm cannot. Additionally, its average segmentation error rate was 13.23%, which is 2.71% higher than that of the k-means algorithm and 2.95% lower than that of the chromatic aberration algorithm. In conclusion, the proposed method contributes to the vision system of apple-picking robots to locate foreground apple targets quickly and accurately under a natural scene.Additional key words:picking robots; Malus domestica; ROI; Itti model.
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