Maize disease segmentation method based on improved image segmentation network model

Yong Yang; HaoJi Shan; FuHeng Qu

doi:10.1117/12.2671267

28 April 2023 Maize disease segmentation method based on improved image segmentation network model

Yong Yang, HaoJi Shan, FuHeng Qu

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 1261020 (2023) https://doi.org/10.1117/12.2671267
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

Deep learning segmentation networks can effectively solve the crop disease segmentation problem, but maize diseases usually vary greatly in size, leading to their unsatisfactory segmentation accuracy. To address this problem, this paper proposes an improved Deeplabv3+ segmentation network model. First, in the encoding stage, feature extraction using Resnet101 with atrous convolution, and the Jump-Connected Atrous Spatial Pyramid Pooling (JCASPP) module is designed to obtain multi-scale semantic information. Second, in the decoding stage, the JCASPP output is fused with the shallow features of the backbone network to obtain richer spatial information by using multilayer and small multiplicative up sampling. The comparison experimental results with the traditional DeepLabv3+ model and its two improved models show that the segmentation accuracy of this model is higher, and the average cross-merge ratio reaches 77.3%.

Citation Download Citation

Yong Yang, HaoJi Shan, and FuHeng Qu "Maize disease segmentation method based on improved image segmentation network model", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 1261020 (28 April 2023); https://doi.org/10.1117/12.2671267

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KEYWORDS

Image segmentation

Diseases and disorders

Semantics

Deep learning

Education and training

Ablation

Convolution

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