Current Issue : October-December Volume : 2021 Issue Number : 4 Articles : 5 Articles
In 2014, we designed and implemented BeePi, a multi-sensor electronic beehive monitoring system. Since then we have been using BeePi monitors deployed at different apiaries in northern Utah to design audio, image, and video processing algorithms to analyze forager traffic in the vicinity of Langstroth beehives. Since our first publication on BeePi in 2016, we have received multiple requests from researchers and practitioners for the datasets we have used in our research. The main objective of this article is to provide a comprehensive point of reference to the datasets that we have so far curated for our research. We hope that our datasets will provide stable performance benchmarks for continuous electronic beehive monitoring, help interested parties verify our findings and correct errors, and advance the state of the art in continuous electronic beehive monitoring and related areas of AI, machine learning, and data science....
With the increasing use of Internet technologies, image data is spreading more and more on the Internet. Whether it is a social network or a search engine, a large amount of image data is generated. By studying the distributed network image processing system and transmission control algorithm, this paper proposes a more accurate gradient calculation method based on the SIFT algorithm. It is concluded that the performance of the proposed algorithm is slightly better than that of the original algorithm, so the system is implemented. On the basis of reducing the performance of the original algorithm, the dimension of the image features is effectively reduced. By comparing the influence of the image retrieval system in the single-machine environment and the distributed environment on the image feature extraction rate, it is proved that the system uses five distributed nodes to construct the image transmission system that achieves the best results in terms of machine cost and system performance. ,e random Gaussian orthogonal matrix is analyzed with good stability and performance. ,e OMP algorithm has good convergence and reconstruction performance. ,e MH-BCS-SPL reconstruction algorithm works best, and the PSNR decreases very smoothly in the process of increasing the packet loss rate from 0.1 to 0.6. ,e experimental results show that different orthogonal bases behave differently under different images. Overall, the BCS-SPL series algorithm has greatly improved the reconstruction effect compared with the traditional OMP algorithm....
Video object detection still faces several difficulties and challenges. For example, the imbalance of positive and negative samples leads to low information processing efficiency, and detection performance declines in abnormal situations in video. This paper examines video object detection based on local attention to address such challenges. We propose a local attention sequence model and optimized the parameter and calculation of ConvGRU. It could process spatial and temporal information in videos more efficiently and ultimately improve detection performance under abnormal conditions. The experiments on ImageNet VID show that our method could improve the detection accuracy by 5.3%, and the visualization results show that the method is adaptive to different abnormal conditions, thereby improving the reliability of video object detection....
When agricultural automation systems are required to send cultivation field images to the cloud for field monitoring, pay-as-you-go mobile communication leads to high operation costs. To minimize cost, one can exploit a characteristic of cultivation field images wherein the landscape does not change considerably besides the appearance of the plants. Therefore, this paper presents a method that transmits only the difference data between the past and current images to minimize the amount of transmitted data. This method is easy to implement because the difference data are generated using an existing video encoder. Further, the difference data are generated based on an image at a specific time instead of the images at adjacent times, and thus the subsequent images can be reproduced even if the previous difference data are lost because of unstable mobile communication. A prototype of the proposed method was implemented with a MPEG-4 Visual video encoder. The amount of transmitted and received data on the medium access control layer was decreased to approximately 1/4 of that when using the secure copy protocol. The transmission time for one image was 5.6 s; thus, the proposed method achieved a reasonable processing time and a reduction of transmitted data....
As one of the most widely used methods in deep learning technology, convolutional neural networks have powerful feature extraction capabilities and nonlinear data fitting capabilities. However, the convolutional neural network method still has disadvantages such as complex network model, too long training time and excessive consumption of computing resources, slow convergence speed, network overfitting, and classification accuracy that needs to be improved. )erefore, this article proposes a dense convolutional neural network classification algorithm based on texture features for images in virtual reality videos. First, the texture feature of the image is introduced as a priori information to reflect the spatial relationship between pixels and the unique characteristics of different types of ground features. Second, the grey level cooccurrence matrix (GLCM) is used to extract the grey level correlation features of the image in space. )en, Gauss Markov Random Field (GMRF) is used to establish the statistical correlation characteristics between neighbouring pixels, and the extracted GLCM-GMRF texture feature and image intensity vector are combined. Finally, based on DenseNet, an improved shallow layer dense convolutional neural network (L-DenseNet) is proposed, which can compress network parameters and improve the feature extraction ability of the network. )e experimental results show that compared with the current classification method, this method can effectively suppress the influence of coherent speckle noise and obtain better classification results....
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