With the rapid development of UAV technology and aerial surveillance systems, efficient and accurate aircraft inspection techniques have become increasingly important. In this paper, we propose an improved YOLOv8-based model for improving the accuracy and efficiency of aircraft detection. We integrate the Double Attention mechanism to capture the key features of the aircraft more efficiently; employ the Adaptive Feature Pyramid Network (AFPN) for multi-scale feature processing to improve the detection capability for aircraft of different sizes; and introduce the Multi-Path Decoupled Intersection over Union (MPDIOU) to optimize the accuracy of the bounding box. Through a series of experiments on a standard aircraft inspection dataset, our model can achieve an average precision mean of 96.8% in real-time aircraft inspection, while the recall rate can reach 95%. Our model demonstrates a significant improvement in various key performance metrics, which proves the potential and effectiveness of the proposed method in practical applications.
Aerospace Aircraft Inspection Based on Improved YoloV8
2023-11-17
862223 byte
Conference paper
Electronic Resource
English