基于图像特征融合的苹果在线分级方法  

Online apple grading based on decision fusion of image features

作　　者：黄辰[1] 费继友[1] Huang Chen Fei Jiyou(School of Mechanical Engineering, Dalian Jiaotong University, Dalian 116028, China)

机构地区：[1]大连交通大学机械工程学院,大连116028

出　　处：《农业工程学报》2017年第1期285-291,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金资助项目(51376028);国家科技支撑计划(2015BAF20B02)

摘　　要：苹果在线分级是提升苹果商品化价值的重要环节,需要同时满足分级准确度和速度要求。为进一步提高苹果在线分级效率,该文借助机器视觉技术动态采集苹果传输过程中的实时图像,提出改进的三层Canny边缘检测算法来提取苹果轮廓以克服采集图像中的光线噪声影响,通过分析苹果分级指标,采用判别树对苹果的果径、缺陷面积、色泽等特征进行初步分级判断,并采用粒子群参数优化的支持向量机对果形、果面纹理、颜色分布等特征进行模型构建与分级,最后,通过将两种分级判断结果进行决策融合来实现样本精确分级。同时,采取图像压缩和特征降维方法提高实时性。试验结果表明,基于图像特征决策融合的苹果分级准确率可达到95%,平均分级速率可达到4个/s。研究结果为水果的在线分级提供参考。Machine vision has been applied in automatic grading for fruit recently. It is well known that as an important detection task, online fruit grading automation faces some challenges in online sorting system. Machine vision is a developing technique facilitating the fruit detection. In this paper, in order to improve the accuracy and efficiency for apple grading, an online detection method of apple grading based on machine vision features is presented. Apple images can be captured from the online industrial camera in the sorting system, which can ensure that the imaging view is suitable for the object area of the conveyor. At first, to obtain the relationship between the real size and the image size, the improved calibration algorithm with the standard workpiece is used to calculate the pixel equivalent of the captured image. The distance regularized level set method is introduced to segment the workpiece edge, and the best-fit rectangle method is used to compute the length and width of the pixels. Then the values of pixel equivalent in length and width are respectively obtained. Secondly, because apple grading detections are mostly performed in indoors, illumination can seriously affect the detection accuracy. For this problem mentioned above, the image preprocessing including wavelet compression and median filtering is introduced to reduce the size and overcome the noise which is from the image acquiring and transmission previously. Accordingly, the improved three-layer Canny edge detection algorithm is proposed to extract the apple contour for the online image which suffers from the uneven lighting. In this step, the 3 different thresholds and scales are considered. The morphological operation is used to close the edge. Thirdly, according to the characteristics of the appearance class of the Fuji apple, multi-feature parameters can be considered, which include fruit diameter, defeat area, color degree, fruit shape, texture features, color distribution parameters and so on. Decision tree is used to determine the

关 键 词：图像处理 支持向量机 机器视觉 苹果分级 决策融合 三层Canny 特征分析 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]