An efficient automobile assembly state monitoring system in industrial environment is presented in this paper. The system only needs to input a video that contains the whole detected parts and manually label it in the...
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An efficient automobile assembly state monitoring system in industrial environment is presented in this paper. The system only needs to input a video that contains the whole detected parts and manually label it in the first frame. By finding the best point for tracking and tracking the point, the dataset can be automatically generated which saves time spent on manufacturing the dataset and makes the assembly state monitoring system easy to deploy into a practical industrial environment. The target detection algorithm uses the channel-pruned YOLOv4 neural network. The experimental result shows the algorithm balances speed and accuracy. Compared to original YOLOv4, the proposed method is two times faster and the performance is nearly equal to it. Comparative experiments show that the proposed algorithm performs better and is faster than other lightweight models which demonstrates that the channelpruning process dynamically improves the speed of the forward propagation without sacrificing accuracy. Additionally, the algorithms are deployed on two common embedded systems. The results show that in the industrial environment, the speed can fully meet real-time requirements.
Achieving the rapid and accurate detection of apple flowers in natural environments is essential for yield estimation and the development of an automatic flower thinner. A real-time apple flower detection method using...
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Achieving the rapid and accurate detection of apple flowers in natural environments is essential for yield estimation and the development of an automatic flower thinner. A real-time apple flower detection method using the channel pruned YOLO v4 deep learning algorithm was proposed. First, the YOLO v4 model under the CSPDarknet53 framework was built, and then, to simplify the apple flower detection model and ensure the efficiency of the model, the channel pruning algorithm was used to prune the model. Finally, a total of 2230 manually labeled apple flower images (including three varieties of Fuji, Red Love, and Gala) were used to fine-tune the model to achieve the fast and accurate detection of apple flowers. The test results showed that the number of parameters of the apple flower detection model after pruning was reduced by 96.74%, the model size was reduced by 231.51 MB, the inference time was decreased by 39.47%, and the mAP was 97.31%, which was only 0.24% lower than the model before pruning. To verify the effectiveness of the proposed method, five different deep learning algorithms including the Faster R-CNN, Tiny-YOLO v2, YOLO v3, SSD 300 and EfficientDet-D0 were compared. The comparative results showed that the mAP of the apple flower detection using the proposed method was 97.31%;the detection speed was 72.33f/s;the model size was 12.46 MB;the mAP was 12.21%, 15.56%, 14.19%, 5.67% and 7.79% higher than the other five algorithms, respectively;and the detection speed could meet the real-time requirements. Furthermore, the detection performance of apple flowers under different species of apple trees and illumination conditions was discussed. The results indicated that the proposed method had strong robustness to the changes of fruit tree varieties and illumination directions. The results showed that it was feasible to apply the proposed method for the real-time and accurate detection of apple flowers. The research could provide technical references for orchard
Traditional maize ear harvesters mainly rely on manual identification of fallen maize ears,which cannot realize real-time detection of ear *** improved You Only Look Once-V4(YOLO-V4)algorithm is combined with the chan...
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Traditional maize ear harvesters mainly rely on manual identification of fallen maize ears,which cannot realize real-time detection of ear *** improved You Only Look Once-V4(YOLO-V4)algorithm is combined with the channel pruning algorithm to detect the dropped ears of maize harvesters.K-means clustering algorithm is used to obtain a prior box matching the size of the dropped ears,which improves the Intersection Over Union(IOU).Compare the effect of different activation functions on the accuracy of the YOLO-V4 model,and use the Mish activation function as the activation function of this *** the calculation of the regression positioning loss function,and use the CEIOU loss function to balance the accuracy of each *** improved Adam optimization function and multi-stage learning optimization technology to improve the accuracy of the YOLO-V4 *** channel pruning algorithm is used to compress the model and distillation technology is used in the fine-tuning of the *** final model size was only 10.77%before compression,and the test set mean Average Precision(mAP)was 93.14%.The detection speed was 112 fps,which can meet the need for real-time detection of maize harvester ears in the *** study can provide technical reference for the detection of the ear loss rate of intelligent maize harvesters.
In order to avoid safety problems caused by foreign bodies such as mice that may appear in the power distribution room and by demarcating the electronic fence area for key monitoring in the video surveillance screen, ...
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In order to avoid safety problems caused by foreign bodies such as mice that may appear in the power distribution room and by demarcating the electronic fence area for key monitoring in the video surveillance screen, a foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved YOLOv4 deep learning network is proposed. To optimize the detection effects, the YOLOv4 algorithm is improved from the aspects of network structure, frame detection, and loss function. At the same time, the channel pruning algorithm is used to prune the model to simplify the model structure. The experimental results show the effectiveness of the improved YOLOv4 deep learning network, which has high detection accuracy, fast detection speed, and takes up less space after pruning.
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