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

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

Waste detection is one of the main problems preventing the realization of automated waste classification, which is a basic function for robotic arms. In addition to object identification in general image analysis, a waste-sorting robotic arm not only needs to identify a target object but also needs to accurately judge its placement angle so that it can determine an appropriate angle for grasping. In order to solve the problem of low-accuracy image detection caused by irregular placement angles, in this work, we propose an improved oriented waste detection method based on YOLOv5. By optimizing the detection head of the YOLOv5 model, this method can generate an oriented detection box for a waste object that is placed at any angle. Based on the proposed scheme, we further improved three aspects of the performance of YOLOv5 in the detection of waste objects: the angular loss function was derived based on dynamic smoothing to enhance the model’s angular prediction ability, the backbone network was optimized with enhanced shallow features and attention mechanisms, and the feature aggregation network was improved to enhance the effects of feature multi-scale fusion. The experimental results showed that the detection performance of the proposed method for waste targets was better than other deep learning methods. Its average accuracy and recall were 93.9% and 94.8%, respectively, which were 11.6% and 7.6% higher than those of the original network, respectively.

/pdf/improved-method-for-oriented-waste-detection-2vmpcap9.pdf

Improved Method for Oriented Waste Detection

Vespa velutina is an ecosystem disruptor that causes annual damage worth KRW 170 billion (USD 137 million) to the South Korean beekeeping industry. Due to its strong fertility and high-lying habitat, it is difficult to control. This study aimed to develop a system for the control of V. velutina nests using drones for detection and tracking the real-time location of the nests. Vespa velutina nest image data were acquired in Buan-gun and Wanju-gun (Jeollabuk-do), and artificial intelligence learning was conducted using YOLO-v5. Drone image resolutions of 640, 1280, 1920, and 3840 pixels were compared and analyzed. The 3840-pixel resolution model was selected, as it had no false detections for the verification image and showed the best detection performance, with a precision of 100%, recall of 92.5%, accuracy of 99.7%, and an F1 score of 96.1%. A computer (Jetson Xavier), real-time kinematics module, long-term evolution modem, and camera were installed on the drone to acquire real-time location data and images. Vespa velutina nest detection and location data were delivered to the user via artificial intelligence analysis. Utilizing a drone flight speed of 1 m/s and maintaining an altitude of 25 m, flight experiments were conducted near Gyeongcheon-myeon, Wanju-gun, Jeollabuk-do. A total of four V. velutina nests were successfully located. Further research is needed on the detection accuracy of artificial intelligence in relation to objects that require altitude-dependent variations in drone-assisted exploration. Moreover, the potential applicability of these research findings to diverse domains is of interest.

Development of a Real-Time Vespa velutina Nest Detection and Notification System Using Artificial Intelligence in Drones

Rapid and noncontact identification of soybean flour in edible insect using NIR spectral imager: A case study in Protaetia brevitarsis seulensis powder

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

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

Development of a Real-Time Vespa velutina Nest Detection and Notification System Using Artificial Intelligence in Drones