CAJ | 학술논문

为了满足水果采摘机器人对图像分割算法实时性和自适应性的要求，在传统演化算法的基础上，提出了一种基于蜂王交配结合精英选择、截断选择分阶段的改进演化算法对水果图像进行分割。在设计选择策略时，将迭代过程划分为前中后3个阶段，分别采用蜂王交配算法、精英选择策略和截断选择策略来进行适应值的选择，这样既保证了种群的多样性，又克服了传统演化算法局部最优、收敛过快的缺点。试验结果表明，该文提出的水果图像演化分割算法无论从稳定性、分割效果，还是全局最优收敛速度上，都明显优于传统演化算法，分割的阈值稳定在3个像素之内；与Otsu算法、贝叶斯分类算法、K均值聚类算法、模糊C均值算法等其他算法相比，水果图像演化分割算法分割效果最好，对同一幅图像进行分割得到的分割识别面积参考值最大，而且运行速度最快，平均运行时间为0.08735s，远少于其余4种算法；并能用于柑橘、荔枝、苹果等各种水果的图像分割，具有一定的通用性，达到水果采摘机器人视觉实时识别的要求，为水果图像分割及其实时获取提供了一种新的基础算法。
위료만족수과채적궤기인대도상분할산법실시성화자괄응성적요구，재전통연화산법적기출상，제출료일충기우봉왕교배결합정영선택、절단선택분계단적개진연화산법대수과도상진행분할。재설계선택책략시，장질대과정화분위전중후3개계단，분별채용봉왕교배산법、정영선택책략화절단선택책략래진행괄응치적선택，저양기보증료충군적다양성，우극복료전통연화산법국부최우、수렴과쾌적결점。시험결과표명，해문제출적수과도상연화분할산법무론종은정성、분할효과，환시전국최우수렴속도상，도명현우우전통연화산법，분할적역치은정재3개상소지내；여Otsu산법、패협사분류산법、K균치취류산법、모호C균치산법등기타산법상비，수과도상연화분할산법분할효과최호，대동일폭도상진행분할득도적분할식별면적삼고치최대，이차운행속도최쾌，평균운행시간위0.08735s，원소우기여4충산법；병능용우감귤、려지、평과등각충수과적도상분할，구유일정적통용성，체도수과채적궤기인시각실시식별적요구，위수과도상분할급기실시획취제공료일충신적기출산법。
An improved evolutionary algorithm based on queen mating combined with elite and truncated choices by stages was proposed for fruit image segmentation, which was appropriate for the demand of the picking robot for real-time image and adaptive processing algorithms. The 8 bit binary code was used to correspond with the gray value of the fruit image in the improved evolutionary algorithm. The number of the initial population was set to 12 in the phase of the population initialized and the corresponding individual values, which ranged between 0 and 255, were generated by the random function. The twelve random numbers were the initial values of the evolutionary algorithm. Then an improved Otsu algorithm formula was selected as the fitness function. In the selection phase, the iterative process was divided into before stage, middle stage, and after stage, which were respectively used by queen mating algorithm, elitist choices strategy, and truncated choices strategy to select the fitness value. In the first stage, the individuals were produced by a random function and then the best individual (queen) of the evolutionary algorithm was hybridized with the rest of the individuals (including the randomly generated individuals) to generate new individuals. Finally, the individuals with the smallest fitness values were replaced by the new individuals. In the second stage, the elitist choices strategy was used and the individual with the smallest fitness value in the current generation was replaced by the individual with the highest fitness value in the previous generation. In the third stage, the truncated choices strategy was used and the last half of the individuals with the smallest fitness value in the current generation was replaced by the same number of individuals with the highest fitness value in the previous generation. This not only ensures the diversity of the population, but also overcomes the disadvantage of local optimized and too fast a convergence of the traditional evolutionary algorithm. In the crossover phase, it uses a single-point crossover method. In the mutation phase, the selected mutation probability was 0.2, which was obtained by comparing the results of different experiments. In the termination phase, the termination condition of the evolutionary algorithm in this paper was that the number of the current iteration had reached the maximum number set by the user in advance. The experimental results showed that the proposed fruit image evolutionary segmentation algorithm was obviously superior to the traditional evolutionary algorithm, and was better in terms of stability, segmentation effect, running speed, etc, and the segmentation threshold value was stabilized within three pixels. Compared with the Otsu segmentation algorithm, K-means clustering segmentation algorithm, fuzzy C-means clustering segmentation algorithm, and Bayesian classification segmentation algorithm, the fruit image evolutionary segmentation algorithm was the best segmentation effect and had the least run time. The average run time of the evolutionary algorithm was 0.08735 seconds, which was less than the other 4 algorithms. The evolutionary segmentation algorithm could be used for citrus, litchi, apple, and other fruits image segmentation and so the algorithm has certain universal utility. The algorithm was achieved by the demand of vision real-time positioning of the fruit picking robot and had provided a new basis algorithm for the image segmentation and its real-time research.