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This thesis introduces a nondestructive inspection and weight grading device for chicken wings to replace the traditional manual grading operation. A two-sided quality nondestructive inspection model of chicken wings based on the YOLO v7-tiny target detection algorithm is designed and deployed in a Jetson Xavier NX embedded platform. An STM32 microcontroller is used as the main control platform, and a wing turning device adapting to the conveyor belt speed, dynamic weighing, and a high-efficiency intelligent grading unit are developed, and the prototype is optimized and verified in experiments. Experiments show that the device can grade four chicken wings per second, with a comprehensive accuracy rate of 98.4%, which is better than the traditional grading methods in terms of efficiency and accuracy.
Wang et al. (Tue,) studied this question.