Aiming at the low-efficiency and high-cost manual sorting status of the assembly line and the need to select a specific product among many goods, a conveyor belt sorting device based on the color, shape, label and characteristics of the goods is designed. According to the design requirements of the system, the detection principles of the Kirchhoff circle detection algorithm and the AprilTag labeling algorithm are analyzed, and the trained model is deployed to the embedded terminal based on the TensorFlow Lite parser. Finally, combined with the STC90C52 microcontroller, a new method based on the characteristics of the goods is proposed. The method of classifying with different algorithms not only improves the operation efficiency of the equipment but also reduces the sorting error rate. The experimental results show that the device can sort all kinds of goods efficiently and achieve the expected design effect.
KEYWORDS: Convolutional neural networks, Image processing, Digital imaging, System identification, Image segmentation, Convolution, Cameras, Statistical modeling, Data modeling, Control systems
In order to solve the key problem of hop count detection of digital vernier calipers before leaving the factory, and the traditional manual detection method requires high labor and is inefficient, a digital caliper based on convolutional neural network and OpenCV computer vision library is designed. Identify automatic detection systems. Firstly, according to the spatial position and size distribution characteristics of the digital display screen of the digital vernier caliper, the digital display area image of the caliper is collected by calling a drive-free USB industrial camera in opencv-python, and preprocessed, and then the convolutional neural network is used. The (CNN) model conducts data training on the image samples of the digital display area, and finally the test part conducts a digital recognition detection experiment of 20 frames per second on the digital display area. The experimental results show that the digital vernier caliper automatic identification and detection system based on the convolutional neural network model has a digital recognition accuracy rate of 99.32%, and the accuracy fluctuation is only 0.68%, which better realizes the need to log the digital vernier caliper before leaving the factory. Hop detection is performed for this purpose.
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