YOLO-Class: Detection and Classification of Aircraft Targets in Satellite Remote Sensing Images Based on YOLO-Extract

• Published in: IEEE access Vol. 11; pp. 109179 - 109188
• Main Authors: Liu, Zhiguo, Gao, Yuan, Du, Qianqian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Aircraft; Aircraft detection; Atmospheric modeling; Classification; Computational modeling; Earth surface; Feature extraction; Model accuracy; Modules; Occlusion; Remote sensing; representative batch normalization; RepVGG; Satellite imagery; Satellite remote sensing images; Satellites; Target detection; Training; VariFocal loss; YOLO-extract
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3321828

With the continuous advancement of remote sensing technology, satellite remote sensing images have become one of the important means of obtaining information on the earth surface. But in the current research on aircraft target detection and classification in remote sensing images, the imbalanced data samples, large variations in target scales and backgrounds, and target occlusion have led to low average precision and slow detection speed in detection and classification tasks. Therefore, this paper proposes the YOLO-class model. Firstly, the YOLO-Extract model is transferred to optimize the detection of small targets, dense targets, and occluded targets. Secondly, Representative Batch Normalization and Mish activation function are used to optimize the Conv module, and VariFocal loss is used to optimize the classification loss function to improve the accuracy caused by imbalanced data samples. Finally, RepVGG modules are designed in the Backbone to further improve the detection accuracy of the model. Simulation results show that compared with the YOLO-Extract model, YOLO-class improves the detection accuracy from 0.608 to 0.704 and FPS from 36.16 to 39.598.