Leaf image segmentation based on DeepLabV3+ and channel attention mechanism

Xin Jiang; Menglin Chen; Wenbo Wang; Lv Qing; Wenxing He; Yi Cao

doi:10.1117/12.3026834

27 March 2024 Leaf image segmentation based on DeepLabV3+ and channel attention mechanism

Xin Jiang, Menglin Chen, Wenbo Wang, Lv Qing, Wenxing He, Yi Cao

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131050C (2024) https://doi.org/10.1117/12.3026834
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

Image segmentation holds significant research significance and practical applications in the fields of computer vision and image processing, particularly in agricultural production, environmental monitoring, and digital botany. However, there is relatively limited research on its application to paper mulberry, prompting us to construct our own semantic segmentation dataset for paper mulberry. This dataset comprises 2576 images, which were meticulously annotated using the eiseg tool. Data augmentation techniques were also employed during the training process. Regarding the model, this study is built upon the DeepLabV3+ architecture, with the introduction of an attention mechanism known as the Squeeze-and-Excitation Block (SE-Block). Furthermore, modifications to the dilation rates of atrous convolutions within the ASPP module were made to achieve a larger receptive field. Experimental results demonstrate a notable improvement in segmentation accuracy, with a gain of 0.8% over the original algorithm. This enhancement is particularly beneficial for precise leaf area calculations.

Citation Download Citation

Xin Jiang, Menglin Chen, Wenbo Wang, Lv Qing, Wenxing He, and Yi Cao "Leaf image segmentation based on DeepLabV3+ and channel attention mechanism", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131050C (27 March 2024); https://doi.org/10.1117/12.3026834

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KEYWORDS

Image segmentation

Statistical modeling

Agriculture

Convolution

Data modeling

Image enhancement

Image processing

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