Humans can easily learn to recognize every object in life, every landscape, and describe the things around them in detail from the process of growing up, but computers cannot. How to make computers learn to describe things in pictures has become the research direction of many scholars. If this technology is mature, it will bring great boon to people with visual impairments. They can understand the things around them and the beautiful earth through hearing. Robots recognize objects and understand their surroundings. With the development of artificial intelligence, the power of convolutional neural networks is more and more comparable to that of the human brain. In recent years, many scholars have proposed different methods to seek better solutions to this problem, including generative adversarial networks. Based on the classic structure of Encoder-Decoder, this paper first compares the code implementation and results of ResNet101 as an Encoder on the COCO dataset, and then proposes a new solution that integrates YOLOv5 and LSTM, aiming to improve the model inference speed and inference accuracy.
The forming of the ink droplet is very important in the inkjet control system. It is related to the forming of the ink droplet on the substrate with good printing effect. In the process can use of inkjet ink observation system to observe the ink drops of spray forming process, at the same time, use the LED flash and CCD camera to film the formation of the ink droplets, through the pictures use edge detection algorithm to analyze drops forming condition and flight path, still can use the shape of the Hough transform to detect the drops (mainly used to detect circular drops), and finally by MATLAB/Simulink simulation tool to drops control system simulation analysis, The data of ink droplet forming is analyzed and tested in the ink feeding system.
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