基于改进的LinkNet的苹果叶片图像分割算法  

Apple leaf image segmentation algorithm based on improved LinkNet

作　　者：朱世松[1] 马婉丽 赵理山 郑艳梅[1] 郑先波[2] 芦碧波[1] ZHU Shisong;MA Wanli;ZHAO Lishan;ZHENG Yanmei;ZHENG Xianbo;LU Bibo(College of Computer Science and Technology,Henan Polytechnic University,Jiaozuo 454003,Hennan,China;College of Horticulture,Henan Agricultural University,Zhengzhou 450002,China)

机构地区：[1]河南理工大学计算机科学与技术学院,河南焦作454003 [2]河南农业大学园艺学院,河南郑州450002

出　　处：《浙江农业学报》2023年第1期202-214,共13页Acta Agriculturae Zhejiangensis

基　　金：国家自然科学基金(41871333);河南科技智库调研课题(HNKJZK-2021-56C);河南省教育科学“十四五”规划重点课题(2021JKZD06)。

摘　　要：使用传统方式对苹果叶片进行图像分割进而测量叶片几何参数,虽精度尚可,但效率较低。针对该问题,提出一种基于深度学习语义分割模型和迁移学习的苹果叶片图像分割算法,完成对叶片的快速、准确分割。所提方法以LinkNet为基本网络结构,进行了4个方面的改进:采用ResNet18作为编码器主干网络,融合迁移学习的思想加速模型拟合;减小编码解码块的数量,降低网络复杂度;改进通道约减方案,减少上采样中的参数量;使用子像素卷积进行上采样,降低计算量。结合焦点损失函数,将改进的LinkNet网络应用于标准苹果叶片数据集上。试验结果表明,所提算法的分割精度为97.27%,与原LinkNet相比精度相当;推理时间仅为7.82 ms,相较于原网络缩短39.89%;模型参数量和浮点数计算量大幅减少;且改进网络的推理速度远快于FCN、U-Net、DeepLabV3+等网络。所提算法在快速分割叶片主体的同时,还能较好地保持叶片边缘锯齿等细节特征,能够真正实现高效、精准地分割苹果叶片,为快速测量叶片面积和其他几何参数提供了新的思路。The traditional methods of segmenting apple leaf images and measuring leaf geometric parameters are moderately accurate but inefficient. To address this problem, an apple leaf image segmentation algorithm based on a deep learning semantic segmentation model and transfer learning was proposed to accomplish efficient and accurate segmentation of apple leaves. The proposed method used LinkNet as the base structure, with the following improvements: ResNet18 was utilized as the backbone network of the encoder and incorporates transfer learning ideas to accelerate model fitting;The number of encoder and decoder blocks was reduced to decrease network complexity;The channel reduction scheme was modified to decrease the parameter quantity in up-sampling;The sub-pixel convolution was introduced to replace the final block to reduce computational costs. Combined with the focal loss, the effectiveness of the improved LinkNet was verified on the standard apple leaf dataset. The experimental results showed that the proposed method achieved a segmentation accuracy of 97.27% and an inference time of 7.82 ms, inference time was decreased by 39.89% compared to the original LinkNet with a slight difference in precision, and the parameter quantity and floating point of operations were significantly reduced. In addition, the inference speed of the improved LinkNet was much faster than that of popular methods such as FCN, U-Net and DeepLabV3+. Therefore, the proposed method could segment the leaf body quickly while better maintaining detailed features such as blade edge serrations. It enabled the efficient and accurate segmentation of apple leaves and provided a novel approach to thinking for fast measurement of leaf area and other geometric parameters.

关 键 词：深度学习 语义分割 苹果叶片 LinkNet 子像素卷积 

分 类 号：S682.36[农业科学—观赏园艺]