Authors

Abstract

Aiming at the problems of less research on cherry segmentation and identification, and slow recognition speed and low classification accuracy with agricultural products, a method based on DeepLabV3 was proposed to realize the rapid segmentation and identification of cherry in complex orchard environment. Complex environment mainly includes frontlighting, backlighting, cloudy and rainy days, single fruit, multi fruit, fruit overlap, and branch and leaf occlusion. This model proposed the atrous spatial pyramid pooling (ASPP) module to effectively extract multi-scale contextual information, and solved the problem of target segmentation at multiple scales. The obtained data was divided into training, validation and testing sets in a 7:1:2 ratios, and the ResNet50 was selected as backbone of the network. Experimental results show that the algorithm in this paper can segment cherry quickly and accurately, the mean intersection over union (MIoU) was 91.06%, the mean pixel accuracy (MPA) was 93.05%, and the kappa coefficient was 0.89, which was better than fully convolutional networks (FCN), SegNet, DeepLabV1 and DeepLabV2. It is demonstrated that this study can provide technical support for intelligent segmentation of agricultural products.

Abstract in English

针对当前樱桃分割与识别研究较少，农产品分割与识别速度慢、分类精度低等问题，本文提出一种基于DeepLabV3模型的复杂果园环境下樱桃目标的快速分割与识别方法。DeepLabV3模型提出的孔洞空间金字塔池化模块可有效地提取多尺度语境信息，解决多尺度下的目标分割难题。复杂果园环境主要包括顺光、逆光、阴雨天气、单果、多果、果实重叠和枝叶遮挡等情况。本研究选取ResNet50作为该模型的骨干网络，将获取的图像数据按照7:1:2的比例划分成训练集,验证集和测试集。实验结果表明，本文提出的方法可以对复杂果园背景下的樱桃进行快速准确分割，分割的MIoU值为91.06%，MPA值为93.05%，kappa系数为0.89，均优于FCN、SegNet、DeepLabV1和DeepLabV2方法，该方法能够为农作物智能分割与识别提供技术支持。