Unmanned Aerial Vehicles (UAVs) integrated with imaging technology offer aerial advantages and intelligent computer vision analysis capabilities, making significant contributions in various domains including geoinformatics, agriculture, environmental conservation, and infrastructure surveillance. This integration facilitates efficient and precise data assistance crucial for numerous sectors. However, challenges persist in balancing the scale and accuracy of target recognition, discrepancies in object recognition versus their actual sizes and issues like occlusions. To enhance the efficacy of UAV-based real-time vehicle monitoring, this study integrates an innovative Advanced Vehicle Detection Network (AVDN) into the fundamental network architecture of the YOLOv7-tiny model. This integration aims to enable swift, cost-effective, and accurate vehicle monitoring on roadways. Our findings indicate that the enhanced model achieves a mean Average Precision (mAP) of 56.14%, significantly surpassing the baseline YOLOv7-tiny model. The affordability and efficiency of this model underscore its practical application potential.
Employing YOLOv7-Tiny for Identifying Road Vehicles in Drone-Based Aerial Photography
2024-06-28
6893707 byte
Conference paper
Electronic Resource
English
MS-YOLOv7:YOLOv7 Based on Multi-Scale for Object Detection on UAV Aerial Photography
DOAJ | 2023
|Improved YOLOv7 Target Detection Algorithm Based on UAV Aerial Photography
DOAJ | 2024
|Efficient YOLOv7-Drone: An Enhanced Object Detection Approach for Drone Aerial Imagery
DOAJ | 2023
|YOLOv7-Tiny and YOLOv8n Evaluation for Face Detection
Springer Verlag | 2024
|