分割
人工智能
图像分割
残余物
网(多面体)
特征(语言学)
模式识别(心理学)
路径(计算)
计算机视觉
转化(遗传学)
计算机科学
数学
生物
算法
几何学
哲学
基因
程序设计语言
生物化学
语言学
作者
Shanwen Zhang,Chuanlei Zhang
标识
DOI:10.1016/j.compag.2022.107511
摘要
Early detection and recognition of plant disease is a prerequisite for controlling plant disease, and one of the key steps is to segment plant diseased leaf images. However, this task is challenging because diseased leaf images are often very complex, with irregular shapes, variable sizes, various shapes, rich colors, fuzzy boundaries and messy backgrounds. An improved U-Net (MU-Net) is constructed for plant diseased leaf image segmentation by introducing a residual block (Resblock) and a residual path (Respath). Resblock is introduced into U-Net to overcome gradient disappearance and explosion problems, and 2 Respaths are used instead of 2 skip connections to improve the transformation of corresponding feature information between the contraction path and the expansion path. Furthermore, Resblock and Respath are combined, which can increase the network depth and improve the network's expression ability. Experimental results on a plant diseased leaf image dataset show that the proposed method can improve the accuracy and efficiency of plant diseased leaf image segmentation.
科研通智能强力驱动
Strongly Powered by AbleSci AI