HDTC: Hybrid Model of Dual-Transformer and Convolutional Neural Network from RGB-D for Detection of Lettuce Growth Traits

Automatic detection of lettuce growth traits is of great significance in modern greenhouse cultivation. Existing methods mainly focus on capturing coarse representations from RGB or RGB-D images with learnable convolutional neural networks. However, due to the significant appearance-varying discrepa...

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Bibliographic Details
Published in2023 IEEE International Conference on Image Processing (ICIP) pp. 2185 - 2189
Main Authors Wu, Zhengxian, Liu, Xingpeng, Xue, YiMing, Wen, Juan, Peng, Wanli
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.10.2023
Subjects
Bridges
Couplings
Design methodology
dual-transformer
feature coupling bridge
Feature extraction
Fuses
Growth trait detection
Hydroponics
Visualization
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Summary:Automatic detection of lettuce growth traits is of great significance in modern greenhouse cultivation. Existing methods mainly focus on capturing coarse representations from RGB or RGB-D images with learnable convolutional neural networks. However, due to the significant appearance-varying discrepancies at different growth stages, coarse representations and inefficient depth fusion strategies limit the performance of automatic detection of lettuce growth traits. To alleviate the above problem, this paper proposes a novel detection method for lettuce growth traits based on transformer and convolutional neural network. In this method, we design a dual-transformer module and a residual module to effectively extract multi-scale representations and depth representations from appearance-varying lettuce images. In addition, a feature coupling bridge is proposed to fuse the multi-scale representations and depth representations. The experimental results show that our method outperforms the state-of-the-art methods.
DOI:10.1109/ICIP49359.2023.10222015