Video surveillance as an important function of Internet of Video Things (IoVT) system has been widely used in coal mine monitoring for coal mine safety with excellent results, however, there are still many shortcomings: 1) existing coal mine IoVT systems have limited detection accuracy for small-sized objects; 2) coal mine video surveillance systems generally adopt centralized cloud computing, transmission of massive data causes high latency, which seriously affects the response speed of object detection function; and 3) the concept drift caused by the data stream seriously affect the detection effect of the offline algorithm. To address the above issues, we propose a small object detection method-based federated intelligence (FI) to assist coal mine IoVT for object detection. First, we design a lightweight neural network Rep-ShuffleNet to improve YOLOv8, the state-of-the-art YOLO algorithm, to maintain high detection accuracy while dramatically increasing the inference speed, and with the advantage of lightweight, it can be deployed to embedded devices for low-latency edge computing; Moreover, we design a federated learning (FL)-based multilayer collaborative FL algorithm for local algorithms’ automatic and efficient optimization by asynchronous communication and data interaction reduction strategy. The experimental results show that with the assistance of FI model optimization strategies, the lightweight YOLOv8 has excellent detection performance (mean average precision: 94.6%, APsmall: 86.7%, and frame per second: 21.6), thus to assist coal mine IoVT to realize accurate and real-time underground small object detection.

The power inspection is an important application of UAV-assisted power internet of video things (IoVT) for maintaining the safety of the power system. Due to the limitations of distance and angle, the resolution of the images captured by UAV is low, which seriously impacts the effects of small insulator defects detection. To address this problem, we propose a small-size defects detection method based on multi-scale feature interaction transformer for UAV-assisted Power IoVT. For the algorithm, we design a super-resolution reconstruction-assisted small object detection algorithm, the super-resolution module generates high-resolution images with the requirements of object detection function, which greatly improves the small object detection performance. Moreover, we design multi-scale feature interaction transformer network (MFITN), compared with the traditional non-local attention mechanism, the network structure can capture dependencies in multi-scales features, furthermore, the advantage assist the super-resolution module to generate more realistic image information to further improve small object detection. In addition, we propose a distributed model deployment strategy to deploy our high computational complexity algorithm in the edge side of the IoVT system, which can drive the overall algorithm to perform low-latency edge computation by relying only on the limited computing power devices. Experiments demonstrate that our method has better small object detection performance (mAP=81.3%, FPS=49.7), the super-resolution reconstruction is able to recover more realistic detail information, the distributed computing method can reduce the response latency by 33.4%-87.2%, which all contribute UAV-assisted Power IoVT system to realize accurate and fast power insulator defects detection.

Power inspection in low-illuminance environments is of great significance for ensuring the all-weather stable operation of the power system. However, low visibility at night seriously interferes with the detection performance of small-sized power devices. In response to the issue, we propose a small object real-time detection method for power line inspection in low-illuminance environments. We design an adaptive transformer-ISP (ATISP) module, in which the optimal parameter regression module generates hyperparameters by sensing input image features to guide the image signal processors (ISPs) to perform image enhancement. With the advantage of ISPs, the ATISP has the advantages of fast inference speed and less training cost. Furthermore, the optimal parameter regression module extracts local features and long-distance dependencies through CNN and Transformer to be able to more fully perceive the input image, so that the generated hyperparameters better enhance image defects. In addition, we use lightweight neural network MobileNetv3 to improve YOLOv7, so that the algorithm maintains excellent small object detection performance while significantly increasing the detection speed. Moreover, the integrated model optimisation uses only the object detection loss functions, which allows ATISP to perform image enhancement just according to the object detection needs, improving small object detection effect and shortening the inference time of ATISP. In extensive experiments, compared with 9 state-of-the-art object detection algorithms, our algorithm has the best small-scale insulator faults detection precision (mAP:75.38 $\%$ ) in our DIFE, best small object detection precision (mAP:56.31 $\%$ ) in public dataset Exdark, and faster detection speed (FPS:98.81 and 97.53), which prove our method can achieve fast and accurate low-illuminance insulators detection.

Magnetically coupled resonant (MCR) wireless power transfer (WPT) is a promising technology, which has been widely used in many practical applications. In the practical application environment the misalignments between the transmitting coil and the receiving coil can not be avoided, and it will cause a rapid decline in transmission efficiency. So the misalignments have limited the applications of MCR-WPT technology in many fields. The mathematical formula of transmission efficiency with angular misalignment and lateral misalignment was deduced. And the effects of angular misalignment and lateral misalignment on the transmission efficiency were researched by simulation experiments. And the changing regularity of the transmission efficiency with the change of the angular misalignment and lateral misalignment was obtained. The results show that when there is an angular misalignment between the transmitting coil and the receiving coil, the transmission efficiency can be optimized by adjusting the lateral misalignment between the coils. And when there is a lateral misalignment between the coils, the transmission efficiency can be optimized by adjusting the angular misalignment between the coils. An experimental prototype of MCR-WPT system was built, and experiments had been carried out to evaluate the accuracy of the theoretical analysis. Therefore, the study in this paper lay a theoretical foundation for the analysis of WPT in the complex environment, and also provide a useful reference for the design of WPT system.

With the rapid development of IoT and UAV technology, for the whole IoT system of vehicle detection, the middle and high level of information transmission and server processing has made a breakthrough, so at the bottom of the real-time detection of the vehicle by the UAV is the key to the whole system. However, UAV vehicle detection faces the challenges of too many small targets in the image leading to low detection accuracy, limited hardware platform resources requiring control of model size, and real-time detection requiring high inference speed. Aiming at the above problems, we propose a lightweight model PrFu-YOLO based on YOLOv8 improvement, which achieves a good balance between accuracy, inference speed and model size. And it realises real-time vehicle detection embedded in a UAV platform. To solve the problem of low vehicle detection accuracy, we design a new structure PrFuFPN based on adding a small target detection layer to achieve more advanced feature fusion. To address the limited resources of the platform and the problem of real-time vehicle detection, we add GhostConv to the structure and constantly try to adjust the parameters of the network. Finally, extensive experiments were conducted on the VisDrone2019 and CARPK datasets to fully evaluate the model. Compared to YOLOv8s on the VisDrone2019 test set, mAP50 was improved by 10.05%, mA95 by 14.54%, the number of parameters was reduced by 8.13%, and the model size was reduced by 12.5%, while the FPS of 67.13 fully met the needs of real-time detection.