Forest fires can destroy forest and inflict great damage to the ecosystem. Fortunately, forest fire detection with video has achieved remarkable results in enabling timely and accurate fire warnings. However, the traditional forest fire detection method relies heavily on artificially designed features; CNN-based methods require a large number of parameters. In addition, forest fire detection is easily disturbed by fog. To solve these issues, a lightweight YOLOX-L and defogging algorithm-based forest fire detection method, GXLD, is proposed. GXLD uses the dark channel prior to defog the image to obtain a fog-free image. After the lightweight improvement of YOLOX-L by GhostNet, depth separable convolution, and SENet, we obtain the YOLOX-L-Light and use it to detect the forest fire in the fog-free image. To evaluate the performance of YOLOX-L-Light and GXLD, mean average precision (mAP) was used to evaluate the detection accuracy, and network parameters were used to evaluate the lightweight effect. Experiments on our forest fire dataset show that the number of the parameters of YOLOX-L-Light decreased by 92.6%, and the mAP increased by 1.96%. The mAP of GXLD is 87.47%, which is 2.46% higher than that of YOLOX-L; and the average fps of GXLD is 26.33 when the input image size is 1280 × 720. Even in a foggy environment, the GXLD can detect a forest fire in real time with a high accuracy, target confidence, and target integrity. This research proposes a lightweight forest fire detection method (GXLD) with fog removal. Therefore, GXLD can detect a forest fire with a high accuracy in real time. The proposed GXLD has the advantages of defogging, a high target confidence, and a high target integrity, which makes it more suitable for the development of a modern forest fire video detection system.

/pdf/real-time-forest-fire-detection-by-ensemble-lightweight-1pnzq1y8.pdf

Real-Time Forest Fire Detection by Ensemble Lightweight YOLOX-L and Defogging Method

The use of AI on Smart applications and in the organization of the network edge presents a rapidly advancing research field, with a great variety of challenges and opportunities. This article aims to provide a holistic review of studies from 2019 to 2021 related to the Intelligent Edge, a concept comprising both the use of AI to organize edge networks (Edge Intelligence) and Smart applications in the edge. An introduction is given to the technologies required to understand the state of the art of AI in edge networks, and a taxonomy is provided with “Enabling Technology” for Edge Intelligence, “Organization” of the edge using AI, and AI “Applications” in the edge as its main topics. Research trend data from 2015 to 2020 is presented for various subdivisions of these topics, showing both absolute and relative research interest in each subtopic. The “Organization” aspect, being the main focus of this article, has a more fine-grained subdivision, explaining all contributing factors in detail. The trends indicate an exponential increase in research interest in nearly all subtopics, but significant differences between them. For each subdivision of the taxonomy a number of selected studies from 2019 to 2021 are gathered to form a high-level illustration of the state of the art of Edge Intelligence. From these selected studies and the trend data, a number of short-term challenges and high-level visions for Edge Intelligence are formulated, providing a basis for future work.

/pdf/enabling-and-leveraging-ai-in-the-intelligent-edge-a-review-mb9lgcq9xr.pdf

Enabling and Leveraging AI in the Intelligent Edge: A Review of Current Trends and Future Directions

It is an important topic to research on the federal-learning based smart services to achieve data privacy preservation in the Internet of Vehicles field. However, model training in the vehicles is still confronting the challenge of low learning efficiency when applying the federal-learning concept into the scenario of dense road. To address the above issue, this paper presents a novel technique to enhance the learning efficiency based on traffic density for the Internet of vehicles. First, a double-layered federation architecture is built through coordinating multiple roadside units. The streams of traffic are divided into different regions, where the devices inside each region are federated for down-layer learning. The roadside units corresponding to each region layer are federated for up-layer learning. Second, based on the double-layered federation architecture, an efficient federal-learning algorithm is invented, where the computational overheads of dense traffic are decreased and the data privacy is still preserved during the model training process. Finally, the simulations are conducted using the real-world dataset from the Microscopic vehicular mobility trace of Europarc roundabout, Creteil, France. The simulation results show that the proposed efficient federal-learning algorithm can improve the learning performance and preserve data privacy in the scenario of intensive traffic.

Towards Efficient Learning Using Double-Layered Federation Based on Traffic Density for Internet of Vehicles.

With the widespread adoption of deep neural network approaches in mobile devices, the drawbacks of traditional cloud-based or local deployment solutions are becoming apparent. The high latency caused by cloud-based inferred neural networks and the high power consumption caused by local inference of mobile devices greatly reduce the actual user experience of neural network applications. To solve this problem, this paper proposes a hierarchical deployment and inference acceleration model for deep neural networks based on fog computing. Firstly, a search reallocation of the solution space of a deep neural network is performed, and a Solution Space Tree Pruning (SSTP) based deployment algorithm is designed to select the appropriate network layer for deployment in order to reduce the overall inferred delay of a deep neural network. Next, an algorithm for Maximizing Accuracy based on Guaranteed Latency (MAL) is designed. On the pruned solution-space tree of the SSTP algorithm, the appropriate fog computation nodes are selected for mobile devices in different geographic locations to achieve early exit from the inference task according to the operational delay and inference accuracy requirements of real device terminals. The experimental results show that the proposed solution reduces the average latency of the fog computing-based inference acceleration model by 44.79% compared to the traditional cloud-deployed deep neural network inference, and by 28.75% compared to the edge computing acceleration framework in existing studies. The model meets the minimum latency and accuracy of neural network inference in multiple fog computing scenarios. At the same time, it greatly reduces the performance occupation and case cost of the cloud under the traditional cloud computing model.

Hierarchical deployment of deep neural networks based on fog computing inferred acceleration model

Research based on deep neural networks (DNN) is becoming more common. In order to solve the problem that DNN needs to consume a lot of performance during the use prediction process and generate unacceptable delays for users, a distributed neural network deployment model based on fog computing is proposed. The distributed deployment of deep neural networks in fog computing scenarios is analyzed. A deployment algorithm based on Solution Space Tree Pruning (SSTP) is designed, and a suitable fog computing node deployment model is selected to reduce the delay of prediction tasks. An algorithm for Maximizing Accuracy based on Guaranteed Latency (MAL) is designed and implemented, and suitable fog computing nodes are selected for different tasks to exit the prediction task. Simulation experiment results show that compared with the method of deploying neural network models in the cloud, the model prediction delay of the distributed neural network model based on fog computing is reduced by an average of 44.79%. Reduced the average computing acceleration framework of similar algorithms by 28.75%.

Inference Acceleration Model of Branched Neural Network Based on Distributed Deployment in Fog Computing

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Inference Acceleration Model of Branched Neural Network Based on Distributed Deployment in Fog Computing