Pointer instruments are widely used in the nuclear power industry. Addressing the issues of low accuracy and slow detection speed in recognizing pointer meter readings under varying types and distances, this paper pro...Pointer instruments are widely used in the nuclear power industry. Addressing the issues of low accuracy and slow detection speed in recognizing pointer meter readings under varying types and distances, this paper proposes a recognition method based on YOLOv8 and DeepLabv3+. To improve the image input quality of the DeepLabv3+ model, the YOLOv8 detector is used to quickly locate the instrument region and crop it as the input image for recognition. To enhance the accuracy and speed of pointer recognition, the backbone network of DeepLabv3+ was replaced with Mo-bileNetv3, and the ECA+ module was designed to replace its SE module, reducing model parameters while improving recognition precision. The decoder’s fourfold-up sampling was replaced with two twofold-up samplings, and shallow feature maps were fused with encoder features of the corresponding size. The CBAM module was introduced to improve the segmentation accuracy of the pointer. Experiments were conducted using a self-made dataset of pointer-style instruments from nuclear power plants. Results showed that this method achieved a recognition accuracy of 94.5% at a precision level of 2.5, with an average error of 1.522% and an average total processing time of 0.56 seconds, demonstrating strong performance.展开更多
A learner's stages of L2 development are connected by his or her L1 and culture. It is, accordingly, of paramount impor-tance to understand the second language learners' culture and learning process and better...A learner's stages of L2 development are connected by his or her L1 and culture. It is, accordingly, of paramount impor-tance to understand the second language learners' culture and learning process and better assist them through this process in theway of teaching them English writing.This essay has demonstrated several elements which affect learners' English writing throughmy own experiences, such as different cultures, reading and correct recognition of writing, and the writing process.展开更多
Aiming at people with hearing and speaking obstacles,this paper pro-poses a multialgorithm fusion for gesture recognition.This paper aims to more clearly distinguish easily confused gestures in gesture recognition and...Aiming at people with hearing and speaking obstacles,this paper pro-poses a multialgorithm fusion for gesture recognition.This paper aims to more clearly distinguish easily confused gestures in gesture recognition and improve gesture recognition accuracy by integrating lip-reading recognition.For gesture recognition,this paperfirst performs skin color processing and segmentation on the hand area of the collected video sequence and detects the hand feature points by calling the hand key point model.The extracted gesture features are trained and recognized by the support vector machine algorithm.For lip reading recognition,this paperfirst uses the AdaBoost algorithm to detect and track key points on the collected video sequence,locate the lips,extract the key points of the lips through a convolutional neural network,and input the extracted key point feature sequence into BiLSTM to extract semantic information.The fusion of gesture recognition and lip reading recognition algorithms using the YOLOV5 model can effectively improve the accuracy of gesture recognition.Through experimental verification,the recognition rate can be increased from 89.4%to 94.3%.展开更多
文摘Pointer instruments are widely used in the nuclear power industry. Addressing the issues of low accuracy and slow detection speed in recognizing pointer meter readings under varying types and distances, this paper proposes a recognition method based on YOLOv8 and DeepLabv3+. To improve the image input quality of the DeepLabv3+ model, the YOLOv8 detector is used to quickly locate the instrument region and crop it as the input image for recognition. To enhance the accuracy and speed of pointer recognition, the backbone network of DeepLabv3+ was replaced with Mo-bileNetv3, and the ECA+ module was designed to replace its SE module, reducing model parameters while improving recognition precision. The decoder’s fourfold-up sampling was replaced with two twofold-up samplings, and shallow feature maps were fused with encoder features of the corresponding size. The CBAM module was introduced to improve the segmentation accuracy of the pointer. Experiments were conducted using a self-made dataset of pointer-style instruments from nuclear power plants. Results showed that this method achieved a recognition accuracy of 94.5% at a precision level of 2.5, with an average error of 1.522% and an average total processing time of 0.56 seconds, demonstrating strong performance.
文摘A learner's stages of L2 development are connected by his or her L1 and culture. It is, accordingly, of paramount impor-tance to understand the second language learners' culture and learning process and better assist them through this process in theway of teaching them English writing.This essay has demonstrated several elements which affect learners' English writing throughmy own experiences, such as different cultures, reading and correct recognition of writing, and the writing process.
文摘Aiming at people with hearing and speaking obstacles,this paper pro-poses a multialgorithm fusion for gesture recognition.This paper aims to more clearly distinguish easily confused gestures in gesture recognition and improve gesture recognition accuracy by integrating lip-reading recognition.For gesture recognition,this paperfirst performs skin color processing and segmentation on the hand area of the collected video sequence and detects the hand feature points by calling the hand key point model.The extracted gesture features are trained and recognized by the support vector machine algorithm.For lip reading recognition,this paperfirst uses the AdaBoost algorithm to detect and track key points on the collected video sequence,locate the lips,extract the key points of the lips through a convolutional neural network,and input the extracted key point feature sequence into BiLSTM to extract semantic information.The fusion of gesture recognition and lip reading recognition algorithms using the YOLOV5 model can effectively improve the accuracy of gesture recognition.Through experimental verification,the recognition rate can be increased from 89.4%to 94.3%.
