Small Insulator Defects Detection Based on Multiscale Feature Interaction Transformer for UAV-Assisted Power IoVT

The power inspection is an important application of Unmanned aerial vehicle (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 multiscale feature interaction transformer network (MFITN) 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 MFITN, compared with the traditional nonlocal attention mechanism, the network structure can capture dependencies in multiscales 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
$= \, 81.3\%$
and 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.