Jianfeng Liang
ABSTRACT
Aiming at the problems of poor environmental anti-interference, poor mobility of equipment, slow detection rate, false detection and missed detection in the traditional image recognition method of sprouted potato, this paper proposes a sprouted potato based on a lightweight image deep learning model (MobileNet-YOLOv4). The identification method can improve the accuracy and speed of sprouting potato identification, and at the same time improve the mobility of identification equipment. First, the collected sprouting potato picture sample data is enhanced by CutMix and Mosaic data to improve the generalization of the sample data, and the LabelImg tool is used for image labeling and data set production, and then based on the YOLOv4 model, use the more lightweight MobileNeV3 network structure replaces the main network structure (Backbone) in the original YOLOv4 model, thereby reducing the overall parameter amount of the model, improving the detection speed and the generalization of the model used in the device. Second, optimize the regression box loss function of the model, using the EIoU loss function with higher positioning accuracy is used to improve the accuracy of sprouting potato identification. Finally, the experimental results show that the improved EIoU+MobileNetv3-YOLOv4 model reduces the number of parameters by about 78% compared to the original YOLOv4 model, and the detection speed is improved 28%, the identification speed of sprouted potatoes is faster, the false detection and missed detection rate is lower, and the identification accuracy rate reaches 97.18%, thus providing better technical support for potato automation, high quality and rapid storage.
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Cross Ref
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