关键词: 咖啡豆, 深度学习, 分级, 注意力机制, 多层感知器

Abstract: Aiming at the current problems of grading difficulties and low recognition accuracy of coffee beans, a ShuffleNet V1 coffee bean grading model (ECA—ShuffleNet MLP) incorporating attention mechanism is proposed. The ECA—ShuffleNet MLP model uses ShuffleNet V1 as the backbone network, deletes the maximum pooling layer in the input layer, adds the efficient channel attention (ECA) mechanism after the second ordinary convolution of the ShuffleNet Unit, and finally adds a multi‑layer perceptron module (MLP) as a classification head and Fusion Loss as a loss function. The experimental results on the self‑constructed coffee bean dataset show that the average accuracy of the ECA—ShuffleNe MLP model for grading coffee beans was 97.84%, which compared to the AlexNet, VGG16, MobileNet V1, MobileNet V2, ResNet34, and ResNet50 models, improved by 8.49, 5.41, 3.85, 2.71, 4.16, and 3.20 percentage points. Experimental results on the publicly available coffee bean dataset show that compared to the above models, the ECA—ShuffleNet MLP model graded average accuracy improved by 3.75, 1.00, 10.00, 2.75, 0.08, and 1.25 percentage points. The experimental results on the homemade coffee bean grading and sorting test platform show that the recognition accuracy and grasping success rate are 84.00% and 82.67% when the conveyor belt running speed is 50 mm/s. The ECA—ShuffleNet MLP model has a good grading accuracy and light weight, and it is easy to be deployed on hardware devices with good generalizability.

Key words: coffee beans, deep learning, grading, efficient channel attention, multi?layer perceptron