DT-YOLO: An Improved Object Detection Algorithm for Key Components of Aircraft and Staff in Airport Scenes Based on YOLOv5

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 6; p. 1705
• Main Authors: He, Zhige, He, Yuanqing, Lv, Yang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.03.2025; MDPI
• Subjects: Accuracy; Aeronautics; Aircraft; airport apron; Airports; Algorithms; Civil aviation; Computer vision; Datasets; Deep learning; deformable convolution; dropout; Efficiency; GIoU; Medical imaging equipment; Neural networks; Security management; Telematics; transformer; YOLOv5; Japan; transformer; GIoU; airport apron; YOLOv5; dropout; deformable convolution

With the rapid development and increasing demands of civil aviation, the accurate detection of key aircraft components and staff on airport aprons is of great significance for ensuring the safety of flights and improving the operational efficiency of airports. However, the existing detection models...