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Lightweight sandy vegetation object detection algorithm based on attention mechanism

Authors

Zhongwei Hua
zwhua@suda.edu.cn
https://orcid.org/0000-0003-4881-6060
Applied Technology College of Soochow University, Suzhou; Academy for Engineering and Technology Fudan University, Shanghai, China.
Min Guan
Academy for Engineering and Technology Fudan University, Shanghai, China.

This paper proposes a lightweight sandy vegetation object detection algorithm based on attention mechanism to solve the object detection task in the harsh sandy environment. We reduce the number of model parameters by the lightweight design of the anchor-free object detection algorithm model, thereby reducing the model inference time and memory cost. Specifically, the algorithm uses a lightweight backbone network to extract features and linear interpolation in the neck network to achieve multi-scale. Model algorithm compression is performed by depthwise separable convolution in the head network. At the same time, the channel attention mechanism is added to the model to optimise the algorithm further. Experiments have proved the superiority of the algorithm, the mAP in the training effect is 76%, and the prediction time per frame is 0.0277 seconds. It realises the efficiency and accuracy of the algorithm operation in the desert environment.

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How to Cite

Hua, Z. and Guan, M. (2022) “Lightweight sandy vegetation object detection algorithm based on attention mechanism”, Journal of Agricultural Engineering, 54(1). doi: 10.4081/jae.2022.1471.
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