Studying the real-time face expression state of teachers in class was important to build an objective classroom teaching evaluation system based on AI. However, the face-to-face communication in classroom conditions was a real-time process that operated on a millisecond time scale. Therefore, in order to quickly and accurately predict teachers’ facial expressions in real time, this paper proposed an improved YOLOv5 network, which introduced the attention mechanisms into the Backbone model of YOLOv5. In experiments, we investigated the effects of different attention mechanisms on YOLOv5 by adding different attention mechanisms after each CBS module in the CSP1_X structure of the Backbone part, respectively. At the same time, the attention mechanisms were incorporated at different locations of the Focus, CBS, and SPP modules of YOLOv5, respectively, to study the effects of the attention mechanism on different modules. The results showed that the network in which the coordinate attentions were incorporated after each CBS module in the CSP1_X structure obtained the detection time of 25 ms and the accuracy of 77.1% which increased by 3.5% compared with YOLOv5. It outperformed other networks, including Faster-RCNN, R-FCN, ResNext-101, DETR, Swin-Transformer, YOLOv3, and YOLOX. Finally, the real-time teachers’ facial expression recognition system was designed to detect and analyze the teachers’ facial expression distribution with time through camera and the teaching video.

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Artificial intelligence is a very broad science, which consists of different fields, such as machine learning, and computer vision. In recent years, the world nuclear industry has developed vigorously. At the same time, incidents of loss of radioactive sources also occur from time to time. At present, most of the search for radioactive sources adopt manual search, which is inefficient, and the searchers are vulnerable to radiation damage. Sending a robot to the search an area where there may be an uncontrolled radioactive source is different. Not only does it improve efficiency, it also protects people from radiation. Therefore, it is of great practical significance to design a radioactive source search robot. This paper mainly introduces the design and implementation of a radioactive source intelligent search robot based on artificial intelligence edge computing, aiming to provide some ideas and directions for the research of radioactive source intelligent search robot. In this paper, a research method for the design and implementation of a radioactive source intelligent search robot based on artificial intelligence edge computing is proposed, including intelligent edge computing and gamma-ray imaging algorithms, which are used to carry out related experiments on the design and implementation of radioactive sources, an intelligent search robot based on edge computing. The experimental results of this paper show that the average resolution of the radioactive source search robot is 90.55%, and the resolution results are more prominent.

Intelligent manufacturing is a major trend in manufacturing innovation around the world, and it is also the main direction and key breakthrough point for the transformation and upgrading of the manufacturing industry for a long time now and in the future. Here, we mainly study the role of intelligent manufacturing in defogging unmanned aerial vehicle (UAV) images and how to analyze UAV image defogging methods based on intelligent manufacturing. In recent years, with the continuous progress of UAV technology, UAV project has also been booming. Aerial photographing is one of the most widely used functions of UAV at present. However, when photographing images in severe weather environment such as haze, it will be affected by the absorption and scattering of light by a variety of different suspended substances in the environment, resulting in poor imaging quality, color distortion, fine pitch blur, and other adverse effects. For this reason, there is a large amount of research on the image haze removal of UAV in China. At present, the mainstream methods are based on non-vision sensor and physical model. However, due to the lack of haze removal effect and severe condition limitation in the practical application of the above methods, we explore an innovative set of automatic estimation haze removal method of atmospheric light based on this method, taking the principle of atmospheric estimation direction as the main research direction, and then through the steps of setting the global transmittance, calculating the atmospheric light amplitude, adjusting the image size, and automatically modifying the image condition threshold value, etc. The man-machine image is haze removal. To compare the advantages and disadvantages of the three methods, we choose standard deviation, information entropy, and objective evaluation method to analyze the results of the three methods. Data analysis shows that among the three haze removal methods, the atmospheric light automatic estimation haze removal method has greatly improved the overall haze removal effect. Compared with the previous two kinds of haze removal imaging in the haze environment, it can better guarantee the color balance degree, and also retain more image information, which makes the imaging clarity have a significant improvement, and the overall effect is more natural. This method has played a very good complementary role to the domestic UAV demisting technology.