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A lightweight deep learning network RepYOLO for embedded devices
ZHOU Gan-wei, CHEN Jia-yue, WU Jia-wei, ZHAO Ya-qi, ZHAO Yi-kai, ZHANG Xiao-ying
HUBEI AGRICULTURAL SCIENCES
2024, 63 (8):
153-157.
DOI: 10.14088/j.cnki.issn0439-8114.2024.08.026
A lightweight deep learning network model RepYOLO algorithm was proposed and transplanted to embedded device MCU/MPU. The network model RepYOLO took YOLOv4 as the base network model. By modifying YOLOv4’s backbone network CSPDarkNet to the RepBlock structure, introducing the CBAM attention mechanism in the Neck layer, and replacing the anchor-based detection head with an anchor-free detection head in the head layer along with integrating the ATSS algorithm, the computational load was reduced, and both inference speed and detection accuracy were improved. The experimental results showed that compared with the original YOLOv4 model, the network model RepYOLO showed more significant advantages in wheat spike detection, and its precision rate, recall rate, F1 value and average precision value were increased by 4.7, 3.6, 1.5 and 1.7 percentage points, respectively. In addition, RepYOLO reduced inference time on embedded devices MCU/MPU by 37.03% and 41.44%, respectively.
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