Structurally reparameterized lightweight repseed for efficient classification of naked barley seeds

Shuxiao Wang; Yajie Meng; Fei Gao

doi:10.1117/12.3035307

19 July 2024 Structurally reparameterized lightweight repseed for efficient classification of naked barley seeds

Shuxiao Wang, Yajie Meng, Fei Gao

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132133I (2024) https://doi.org/10.1117/12.3035307
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Naked barley is the fourth largest cereal crop in the world, and its output in Tibet, China accounts for 70% of the country’s total. In China’s highland barley-producing areas, the phenomenon of mixed planting and mixed harvesting of naked barley is common, which makes the refined management of the naked barley industry difficult, thereby reducing the economic value of specialty naked barley. This study proposes a new lightweight network model—RepSeed. RepSeed is based on the depthwise separable convolution and uses a multi-branch structure during the training phase to obtain more detailed features of the image to improve accuracy. During the inference phase, the multi-branch structure is changed into a single-path structure, thereby saving memory space and accelerating the model’s inference speed. Experiments were conducted using a data set containing 9756 images of naked barley seeds. The results show that RepSeed’s TOP-1 accuracy on the data set reached 90.40%, the parameter amount was 0.75M, the FLOPs were 114.6M, the latency was 23 ms, and the GPU memory space occupied was 181MB, the throughput in the GPU environment is 17074im/s.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuxiao Wang, Yajie Meng, and Fei Gao "Structurally reparameterized lightweight repseed for efficient classification of naked barley seeds", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132133I (19 July 2024); https://doi.org/10.1117/12.3035307

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