Real-time object detection based on UAV remote sensing is widely required in different scenarios. In the past 20 years, with the development of unmanned aerial vehicles (UAV), remote sensing technology, deep learning technology, and edge computing technology, research on UAV real-time object detection in different fields has become increasingly important. However, since real-time UAV object detection is a comprehensive task involving hardware, algorithms, and other components, the complete implementation of real-time object detection is often overlooked. Although there is a large amount of literature on real-time object detection based on UAV remote sensing, little attention has been given to its workflow. This paper aims to systematically review previous studies about UAV real-time object detection from application scenarios, hardware selection, real-time detection paradigms, detection algorithms and their optimization technologies, and evaluation metrics. Through visual and narrative analyses, the conclusions cover all proposed research questions. Real-time object detection is more in demand in scenarios such as emergency rescue and precision agriculture. Multi-rotor UAVs and RGB images are of more interest in applications, and real-time detection mainly uses edge computing with documented processing strategies. GPU-based edge computing platforms are widely used, and deep learning algorithms is preferred for real-time detection. Meanwhile, optimization algorithms need to be focused on resource-limited computing platform deployment, such as lightweight convolutional layers, etc. In addition to accuracy, speed, latency, and energy are equally important evaluation metrics. Finally, this paper thoroughly discusses the challenges of sensor-, edge computing-, and algorithm-related lightweight technologies in real-time object detection. It also discusses the prospective impact of future developments in autonomous UAVs and communications on UAV real-time target detection. 

Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review

Sooty mold is a common disease found in citrus plants and is characterized by black fungi growth on fruits, leaves, and branches. This mold reduces the plant’s ability to carry out photosynthesis. In small leaves, it is very difficult to detect sooty mold at the early stages. Deep learning-based image recognition techniques have the potential to identify and diagnose pest damage and diseases such as sooty mold. Recent studies used advanced and expensive hyperspectral or multispectral cameras attached to UAVs to examine the canopy of the plants and mid-range cameras to capture close-up infected leaf images. To bridge the gap on capturing canopy level images using affordable camera sensors, this study used a low-cost home surveillance camera to monitor and detect sooty mold infection on citrus canopy combined with deep learning algorithms. To overcome the challenges posed by varying light conditions, the main reason for using specialized cameras, images were collected at night, utilizing the camera’s built-in night vision feature. A total of 4200 sliced night-captured images were used for training, 200 for validation, and 100 for testing, employed on the YOLOv5m, YOLOv7, and CenterNet models for comparison. The results showed that YOLOv7 was the most accurate in detecting sooty molds at night, with 74.4% mAP compared to YOLOv5m (72%) and CenterNet (70.3%). The models were also tested using preprocessed (unsliced) night images and day-captured sliced and unsliced images. The testing on preprocessed (unsliced) night images demonstrated the same trend as the training results, with YOLOv7 performing best compared to YOLOv5m and CenterNet. In contrast, testing on the day-captured images had underwhelming outcomes for both sliced and unsliced images. In general, YOLOv7 performed best in detecting sooty mold infections at night on citrus canopy and showed promising potential in real-time orchard disease monitoring and detection. Moreover, this study demonstrated that utilizing a cost-effective surveillance camera and deep learning algorithms can accurately detect sooty molds at night, enabling growers to effectively monitor and identify occurrences of the disease at the canopy level.

Sooty Mold Detection on Citrus Tree Canopy Using Deep Learning Algorithms

Vespa velutina nigrithorax, a species of hornet, is spreading globally, with increasingly negative effects on human health. To effectively eliminate V. velutina, its nest should be destroyed and its queen removed; however, the nests are difficult to reach. Thus, we analyzed the requirements for a drone-assisted hornet exterminator using field observations and physical tests on a sample hornets’ nest, and based on these, a UAV exterminator equipped with a nest-perforating device (based on an airsoft rifle) and pesticide-spraying system was designed and manufactured. Pesticides and bullets were manufactured using ecofriendly materials. An actuator at the rear of the device adjusted the pitch of the perforator and sprayer, and a monitoring system was installed to aid the operator in targeting. The operating parameters of the UAV exterminator were evaluated in laboratory tests, with a spray distance of 5 m deemed suitable. To evaluate the system’s pest-control performance, several V. velutina nests were targeted in field tests. An insecticidal effect of over 99% was achieved using two pyrethrum-based pesticides (15% pyrethrum extract and 10% pyrethrum extract with additives). In addition, compared to conventional nest-removal methods, the UAV exterminator reduced the work time by 85% and the cost by 54.9%.

/pdf/exterminator-for-the-nests-of-vespa-velutina-nigrithorax-1np3kynl.pdf

Exterminator for the Nests of Vespa velutina nigrithorax Using an Unmanned Aerial Vehicle

The distribution of transmission lines is complex, influenced by terrain and topography, making their maintenance and management challenging. With the advancement of global mechanization, various types of construction machinery and large‐scale mechanical equipment are continually increasing. Accidental contact of construction machinery near transmission lines poses a serious threat to the stability of the power system. Traditional drones and inspection robots face difficulties in achieving real‐time monitoring of construction machinery and equipment around transmission lines, quickly identifying potential risks. Conventional image processing techniques and convolutional neural networks struggle with effective handling of small targets and densely packed detection tasks involving multiple targets. To address these challenges, this paper proposes an intelligent detection algorithm Swin Transformer attention efficient algorithm‐you only look once (YOLO) (STAE‐YOLO). It is based on the Swin Transformer global self‐attention mechanism, cross‐channel fusion attention mechanism, enhanced small object detection framework, and a focused and efficient regression loss function. The experimental results show that the STAE‐YOLO algorithm model improved by 6.3% in mean average precision, 3.7% in precision, and 3.1% in recall compared to the baseline model. Meanwhile, deploying the window multi‐head self‐attention global self‐attention mechanism in the model can strengthen the global multi‐scale semantic information in detection.

STAE‐YOLO: Intelligent detection algorithm for risk management of construction machinery intrusion on transmission lines based on visual perception

This paper presents an innovative modular steel channel–concrete composite floor system designed for rapid assembly. This system, compared to traditional steel–concrete composite floors, connects adjacent modules solely through the use of high-strength bolts. This design eliminates the need for on-site wet construction, thereby yielding superior economic benefits. To investigate the proposed system's mechanical behavior, bending tests were conducted on four composite floors with various support conditions, the numbers of bolts, and the degrees of shear connection. The load–displacement responses, torsional behavior of the channel beams, interfacial slip, and strain development of the composite floor systems were analyzed. The test results show that the simply supported composite floor, without bolted connections, undergoes flexural failure. In contrast, the semi-continuous composite floors, with bolted connections, display punching shear-flexural failure. The pre-tension forces applied to the bolts can effectively mitigate the torsional deformation of the channels and module separation, ensuring that the system maintains excellent structural integrity, sufficient load-bearing capacity, and adequate ductility. The semi-continuous composite floors, when fitted with high-strength bolts, demonstrate an increase in ultimate load-bearing capacity by 13–15%. However, there is a corresponding decrease in ductility by 18–23% when compared to the simply supported composite floor without bolts. The use of perforated steel plates as shear connectors offers efficient shear resistance between the steel channels and concrete slabs, facilitating full composite action of the system throughout the loading process. The paper concludes with the development of a model that calculates the flexural strength and punching shear strength of the composite floors based on the yield-line theory. 

Flexural behavior of modular steel channel–concrete composite floor systems with bolted connections

Litchi leaf diseases and pests can lead to issues such as a decreased Litchi yield, reduced fruit quality, and decreased farmer income. In this study, we aimed to explore a real-time and accurate method for identifying Litchi leaf diseases and pests. We selected three different orchards for field investigation and identified five common Litchi leaf diseases and pests (Litchi leaf mite, Litchi sooty mold, Litchi anthracnose, Mayetiola sp., and Litchi algal spot) as our research objects. Finally, we proposed an improved fully convolutional one-stage object detection (FCOS) network for Litchi leaf disease and pest detection, called FCOS for Litch (FCOS-FL). The proposed method employs G-GhostNet-3.2 as the backbone network to achieve a model that is lightweight. The central moment pooling attention (CMPA) mechanism is introduced to enhance the features of Litchi leaf diseases and pests. In addition, the center sampling and center loss of the model are improved by utilizing the width and height information of the real target, which effectively improves the model’s generalization performance. We propose an improved localization loss function to enhance the localization accuracy of the model in object detection. According to the characteristics of Litchi small target diseases and pests, the network structure was redesigned to improve the detection effect of small targets. FCOS-FL has a detection accuracy of 91.3% (intersection over union (IoU) = 0.5) in the images of five types of Litchi leaf diseases and pests, a detection rate of 62.0/ms, and a model parameter size of 17.65 M. Among them, the detection accuracy of Mayetiola sp. and Litchi algal spot, which are difficult to detect, reached 93.2% and 92%, respectively. The FCOS-FL model can rapidly and accurately detect five common diseases and pests in Litchi leaf. The research outcome is suitable for deployment on embedded devices with limited resources such as mobile terminals, and can contribute to achieving real-time and precise identification of Litchi leaf diseases and pests, providing technical support for Litchi leaf diseases’ and pests’ prevention and control.

/pdf/detection-of-litchi-leaf-diseases-and-insect-pests-based-on-2ndyxr3k.pdf

Detection of Litchi Leaf Diseases and Insect Pests Based on Improved FCOS

Dynamic Beehive Detection and Tracking System Based on YOLO V5 and Unmanned Aerial Vehicle

This work developed a new steel–concrete composite beam composed of two prefabricated beam-slab modules to achieve rapid assembly, reduce construction costs, and improve structural performance. The modules, comprising C-channels, concrete slabs, and perforated steel plate shear connectors, were connected back-to-back with high-strength bolts, thereby eliminating in-situ wet construction. Seven specimens with various slab widths, beam depths, and shear connector spacings were fabricated and tested under sagging moments to investigate the behavior of the proposed composite beams. The experimental results demonstrated that the tension induced by the high-strength bolts provided excellent cooperation between the beam-slab modules and ensured that the composite beam had a high level of integrity and offered good strength and ductility performance. The bonding between the C-channel and the concrete was robust enough to transfer longitudinal shear forces to the C-channel, allowing for complete shear interactions under service loading. The shear connectors provided efficient composite actions between the C-channel and the concrete slab, resulting in a slight difference in the mechanical properties of the composite beams with different shear connection degrees. As the slab width enlarged, the flexural strength and ductility of the beam enhanced by 8–19% and 11–21% respectively, but the integrity of the two beam-slab modules weakened. Increasing the size of the C-channel was conducive to the integrity of the two modules. The flexural strength of the beam with a large height increased by 96% compared with the specimen with a small height, while its ductility declined by 10%. Finally, this paper proposed simplified models based on Eurocode 4 to calculate the flexural resistance of the composite beams. 

Experimental study on flexural behavior of modular prefabricated channel–concrete composite beams with dry connections

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Detection of Litchi Leaf Diseases and Insect Pests Based on Improved FCOS

Dynamic Beehive Detection and Tracking System Based on YOLO V5 and Unmanned Aerial Vehicle

Experimental study on flexural behavior of modular prefabricated channel–concrete composite beams with dry connections