A YOLOv7-based method for crown extraction from UAV imagery.

In response to the challenges of complex background interference, inadequate feature utilization, and model redundancy in multispectral crown extraction, this paper proposes a dual-channel crown detection and segmentation approach based on an improved YOLOv7 architecture, named Dual-YOLOv7. First, a dual-branch feature extraction network is designed, integrating visible light and infrared spectral information and dynamically weights key features through an attention mechanism. Second, the D-SimSPPF module is introduced, which employs depthwise separable convolution to optimize spatial pyramid pooling, thereby enhancing the capability to capture fine details while reducing the number of parameters.

Research on Enhanced Dynamic Pig Counting Based on YOLOv8n and Deep SORT.

Pig counting is an essential activity in the administration of pig farming. Currently, manual counting is inefficient, costly, and unsuitable for systematic analysis. However, research on dynamic pig counting encounters challenges, including inadequate detection accuracy stemming from crowding, occlusion, deformation, and low-light conditions.

A reliable unmanned aerial vehicle multi-ship tracking method.

As the global economy expands, waterway transportation has become increasingly crucial to the logistics sector. This growth presents both significant challenges and opportunities for enhancing the accuracy of ship detection and tracking through the application of artificial intelligence. This article introduces a multi-object tracking system designed for unmanned aerial vehicles (UAVs), utilizing the YOLOv7 and Deep SORT algorithms for detection and tracking, respectively.

A Shortest Distance Priority UAV Path Planning Algorithm for Precision Agriculture.

Unmanned aerial vehicles (UAVs) have made significant advances in autonomous sensing, particularly in the field of precision agriculture. Effective path planning is critical for autonomous navigation in large orchards to ensure that UAVs are able to recognize the optimal route between the start and end points. When UAVs perform tasks such as crop protection, monitoring, and data collection in orchard environments, they must be able to adapt to dynamic conditions.