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The combination of unmanned aerial vehicles (UAVs) and artificial intelligence is significant and is a key topic in recent substation inspection applications; and meter reading is one of the challenging tasks. This paper proposes a method based on the combination of YOLOv5s object detection and Deeplabv3+ image segmentation to obtain meter readings through the post-processing of segmented images. Firstly, YOLOv5s was introduced to detect the meter dial area and the meter was classified. Following this, the detected and classified images were passed to the image segmentation algorithm. The backbone network of the Deeplabv3+ algorithm was improved by using the MobileNetv2 network, and the model size was reduced on the premise that the effective extraction of tick marks and pointers was ensured. To account for the inaccurate reading of the meter, the divided pointer and scale area were corroded first, and then the concentric circle sampling method was used to flatten the circular dial area into a rectangular area. Several analog meter readings were calculated by flattening the area scale distance. The experimental results show that the mean average precision of 50 (mAP50) of the YOLOv5s model with this method in this data set reached 99.58%, that the single detection speed reached 22.2 ms, and that the mean intersection over union (mIoU) of the image segmentation model reached 78.92%, 76.15%, 79.12%, 81.17%, and 75.73%, respectively. The single segmentation speed reached 35.1 ms. At the same time, the effects of various commonly used detection and segmentation algorithms on the recognition of meter readings were compared. The results show that the method in this paper significantly improved the accuracy and practicability of substation meter reading detection in complex situations.

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Citrus fruit detection can provide technical support for fine management and yield determination of citrus orchards. Accurate detection of citrus fruits in mountain orchards is challenging because of leaf occlusion and citrus fruit mutual occlusion of different fruits. This paper presents a citrus detection task that combines UAV data collection, AI embedded device, and target detection algorithm. The system used a small unmanned aerial vehicle equipped with a camera to take full-scale pictures of citrus trees; at the same time, we extended the state-of-the-art model target detection algorithm, added the attention mechanism and adaptive fusion feature method, improved the model's performance; to facilitate the deployment of the model, we used the pruning method to reduce the amount of model calculation and parameters. The improved target detection algorithm is ported to the edge computing end to detect the data collected by the unmanned aerial vehicle. The experiment was performed on the self-made citrus dataset, the detection accuracy was 93.32%, and the processing speed at the edge computing device was 180 ms/frame. This method is suitable for citrus detection tasks in the mountainous orchard environment, and it can help fruit growers to estimate their yield.

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The demands for magnetic sensors are uprising due to the rapid development of smart equipments and internet of things. Exploring magnetic sensitive materials which are easily obtainable and of low cost thereby become of great significance. Carbon film with crystallized features was recently reported with room-temperature ferro-magnetism and magnetoresistance, owing to its spin-orbital interactions at the graphene edges and temperature-depending carrier transport properties. Such phenomena indicate that the film can serve as a novel magnetic sensitive material. In this study, carbon films with vertically aligned nano-crystallites were obtained by a plasma-assisted physical vapor deposition (PVD) method. Basic test circuits were built on the films, and the sensing properties were investigated in external magnetic fields with different intensities and relative angles to the films surface. The results showed that the carbon-based sensing devices were capable to work in the temperature region of 250-400 K. The minimum field intensity and angle change to which the device can respond were 1 mT and 2°. By substrate-introduced enhancement, the maximum changing-rate of the film resistance could reach to 1100%/T. This research pointed out a practical and simple way to build magnetic sensors with PVD carbon films.