Drone High-Rise Aerial Delivery with Vertical Grid Screening

Seth, Avishkar; James, Alice; Kuantama, Endrowednes; Mukhopadhyay, Subhas Chandra; Han, Richard

doi:10.3390/drones7050300

Cited by 13 publications

(2 citation statements)

References 47 publications

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“….y c ] with attention weights. The specific operation is described in Equation ( 9), where x c (i,j) represents the input feature map, and g h c (i) and g w c (j) denote the attention maps in the horizontal and vertical directions, respectively [28].…”

Section: Bifpn Model Structurementioning

confidence: 99%

Early Drought Detection in Maize Using UAV Images and YOLOv8+

Niu,

Nie,

et al. 2024

Drones

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The escalating global climate change significantly impacts the yield and quality of maize, a vital staple crop worldwide, especially during seedling stage droughts. Traditional detection methods are limited by their single-scenario approach, requiring substantial human labor and time, and lack accuracy in the real-time monitoring and precise assessment of drought severity. In this study, a novel early drought detection method for maize based on unmanned aerial vehicle (UAV) images and Yolov8+ is proposed. In the Backbone section, the C2F-Conv module is adopted to reduce model parameters and deployment costs, while incorporating the CA attention mechanism module to effectively capture tiny feature information in the images. The Neck section utilizes the BiFPN fusion architecture and spatial attention mechanism to enhance the model’s ability to recognize small and occluded targets. The Head section introduces an additional 10 × 10 output, integrates loss functions, and enhances accuracy by 1.46%, reduces training time by 30.2%, and improves robustness. The experimental results demonstrate that the improved Yolov8+ model achieves precision and recall rates of approximately 90.6% and 88.7%, respectively. The mAP@50 and mAP@50:95 reach 89.16% and 71.14%, respectively, representing respective increases of 3.9% and 3.3% compared to the original Yolov8. The UAV image detection speed of the model is up to 24.63 ms, with a model size of 13.76 MB, optimized by 31.6% and 28.8% compared to the original model, respectively. In comparison with the Yolov8, Yolov7, and Yolo5s models, the proposed method exhibits varying degrees of superiority in mAP@50, mAP@50:95, and other metrics, utilizing drone imagery and deep learning techniques to truly propel agricultural modernization.

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Section: Bifpn Model Structurementioning

confidence: 99%

Early Drought Detection in Maize Using UAV Images and YOLOv8+

Niu,

Nie,

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

“…Overall, UAVs have the potential to revolutionize the delivery industry by providing a faster, more efficient, and cost-effective means of delivering goods. However, the technology is still relatively new and there are many regulatory and technical challenges that need to be addressed before UAVs can be widely used for delivery services [159]. Implementing UAV IoT applications requires a combination of technologies, such as sensors, communication systems, and data analytics.…”

Section: Delivery Servicesmentioning

confidence: 99%

Global Models of Smart Cities and Potential IoT Applications: A Review

Hassebo,

Tealab

2023

IoT

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As the world becomes increasingly urbanized, the development of smart cities and the deployment of IoT applications will play an essential role in addressing urban challenges and shaping sustainable and resilient urban environments. However, there are also challenges to overcome, including privacy and security concerns, and interoperability issues. Addressing these challenges requires collaboration between governments, industry stakeholders, and citizens to ensure the responsible and equitable implementation of IoT technologies in smart cities. The IoT offers a vast array of possibilities for smart city applications, enabling the integration of various devices, sensors, and networks to collect and analyze data in real time. These applications span across different sectors, including transportation, energy management, waste management, public safety, healthcare, and more. By leveraging IoT technologies, cities can optimize their infrastructure, enhance resource allocation, and improve the quality of life for their citizens. In this paper, eight smart city global models have been proposed to guide the development and implementation of IoT applications in smart cities. These models provide frameworks and standards for city planners and stakeholders to design and deploy IoT solutions effectively. We provide a detailed evaluation of these models based on nine smart city evaluation metrics. The challenges to implement smart cities have been mentioned, and recommendations have been stated to overcome these challenges.

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Sensors, ML and AI for Real World Applications

Mukhopadhyay,

Jayasundera

2024

Smart Sensors, Measurement and Instrumentation

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Drone High-Rise Aerial Delivery with Vertical Grid Screening

Cited by 13 publications

References 47 publications

Early Drought Detection in Maize Using UAV Images and YOLOv8+

Early Drought Detection in Maize Using UAV Images and YOLOv8+

Global Models of Smart Cities and Potential IoT Applications: A Review

Sensors, ML and AI for Real World Applications

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