Purposes—To design a fast apple target recognition method for improving the target recognition accuracy and recognition efficiency of an apple picking robot in a natural scene in an or-chard.Methods—The CenterNet neural network is used as the detection framework,and a Light-Weight Net lightweight feature extraction network is proposed by drawing on the ideas of grouped convolution and depth-separable convolution.Results—The recognition algorithm adapted to the visual system of apple picking robots has been designed which achieves high-precision and efficient apple tar-get recognition in natural orchard scenes.Conclusions—The model recognized an AP value of 96.60％under the test set,and by comparing with YOLOv3 and Efficient-D0 model in the same test set,the experimental results showed that the AP value was improved by 6.30％and 5.17％,and the average recognition time of a single image was faster by 0.014 s and 0.05 s.
维普
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