Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order t...Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order to solve this problem,we propose a new method,which combined the lightweight network mobile vision transformer(Mobile Vi T)with the convolutional block attention module(CBAM)mechanism and the new regression loss function.This method needed less computation resources,making it more suitable for embedded edge detection devices.Meanwhile,the new loss function improved the positioning accuracy of the bounding box and enhanced the robustness of the model.In addition,experiments on public datasets demonstrate that the improved model achieves an average accuracy of 87.9%across six typical defect detection tasks,while reducing computational costs by nearly 90%.It significantly reduces the model's computational requirements while maintaining accuracy,ensuring reliable performance for edge deployment.展开更多
Deep learning technology is widely used in computer vision.Generally,a large amount of data is used to train the model weights in deep learning,so as to obtain a model with higher accuracy.However,massive data and com...Deep learning technology is widely used in computer vision.Generally,a large amount of data is used to train the model weights in deep learning,so as to obtain a model with higher accuracy.However,massive data and complex model structures require more calculating resources.Since people generally can only carry and use mobile and portable devices in application scenarios,neural networks have limitations in terms of calculating resources,size and power consumption.Therefore,the efficient lightweight model MobileNet is used as the basic network in this study for optimization.First,the accuracy of the MobileNet model is improved by adding methods such as the convolutional block attention module(CBAM)and expansion convolution.Then,the MobileNet model is compressed by using pruning and weight quantization algorithms based on weight size.Afterwards,methods such as Python crawlers and data augmentation are employed to create a garbage classification data set.Based on the above model optimization strategy,the garbage classification mobile terminal application is deployed on mobile phones and raspberry pies,realizing completing the garbage classification task more conveniently.展开更多
Image fire recognition is of great significance in fire prevention and loss reduction through early fire detection and warning.Aiming at the problems of low accuracy of existing fire recognition and high error rate of...Image fire recognition is of great significance in fire prevention and loss reduction through early fire detection and warning.Aiming at the problems of low accuracy of existing fire recognition and high error rate of tiny target detection,this study proposed a fire recognition model based on a channel space attention mechanism.First,the convolutional block attention module(CBAM)is intro-duced into the first and last convolutional layers EfficientNetV2,which shows strong feature extraction ability and high computational efficiency as the backbone network.In terms of channel and space aspects,the weights in the feature layer are increased,which enhances the semantic information of flame smoke features and makes the model pay more attention to the feature information of fire images.Then,label smoothing based on the cross-entropy loss function is introduced into this study to avoid predicting labels too confidently in the training process to improve the generalization ability of the recognition model.The experimental results show that the fire image re-cognition accuracy based on the CBAM-EfficientNetV2 model reaches 98.9%.The accuracy of smoke image recognition can reach 98.5%.The accuracy of small target detection can reach 96.1%.At the same time,we compared the existing methods and found that the proposed method achieved higher accuracy,precision,recall,and F1-score.Finally,the fire image results are visualized using the Grad-CAM technique,which makes the model more effective and more intuitive in detecting tiny targets.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62373215,62373219 and 62073193)the Natural Science Foundation of Shandong Province(No.ZR2023MF100)+1 种基金the Key Projects of the Ministry of Industry and Information Technology(No.TC220H057-2022)the Independently Developed Instrument Funds of Shandong University(No.zy20240201)。
文摘Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order to solve this problem,we propose a new method,which combined the lightweight network mobile vision transformer(Mobile Vi T)with the convolutional block attention module(CBAM)mechanism and the new regression loss function.This method needed less computation resources,making it more suitable for embedded edge detection devices.Meanwhile,the new loss function improved the positioning accuracy of the bounding box and enhanced the robustness of the model.In addition,experiments on public datasets demonstrate that the improved model achieves an average accuracy of 87.9%across six typical defect detection tasks,while reducing computational costs by nearly 90%.It significantly reduces the model's computational requirements while maintaining accuracy,ensuring reliable performance for edge deployment.
文摘Deep learning technology is widely used in computer vision.Generally,a large amount of data is used to train the model weights in deep learning,so as to obtain a model with higher accuracy.However,massive data and complex model structures require more calculating resources.Since people generally can only carry and use mobile and portable devices in application scenarios,neural networks have limitations in terms of calculating resources,size and power consumption.Therefore,the efficient lightweight model MobileNet is used as the basic network in this study for optimization.First,the accuracy of the MobileNet model is improved by adding methods such as the convolutional block attention module(CBAM)and expansion convolution.Then,the MobileNet model is compressed by using pruning and weight quantization algorithms based on weight size.Afterwards,methods such as Python crawlers and data augmentation are employed to create a garbage classification data set.Based on the above model optimization strategy,the garbage classification mobile terminal application is deployed on mobile phones and raspberry pies,realizing completing the garbage classification task more conveniently.
基金National Key Research and Development Program of China(No.2021YFC1523502-03)Fundamental Research Funds for the Central Universities,China(No.FRF-IDRY-21-016).
文摘Image fire recognition is of great significance in fire prevention and loss reduction through early fire detection and warning.Aiming at the problems of low accuracy of existing fire recognition and high error rate of tiny target detection,this study proposed a fire recognition model based on a channel space attention mechanism.First,the convolutional block attention module(CBAM)is intro-duced into the first and last convolutional layers EfficientNetV2,which shows strong feature extraction ability and high computational efficiency as the backbone network.In terms of channel and space aspects,the weights in the feature layer are increased,which enhances the semantic information of flame smoke features and makes the model pay more attention to the feature information of fire images.Then,label smoothing based on the cross-entropy loss function is introduced into this study to avoid predicting labels too confidently in the training process to improve the generalization ability of the recognition model.The experimental results show that the fire image re-cognition accuracy based on the CBAM-EfficientNetV2 model reaches 98.9%.The accuracy of smoke image recognition can reach 98.5%.The accuracy of small target detection can reach 96.1%.At the same time,we compared the existing methods and found that the proposed method achieved higher accuracy,precision,recall,and F1-score.Finally,the fire image results are visualized using the Grad-CAM technique,which makes the model more effective and more intuitive in detecting tiny targets.
