2023
DOI: 10.3390/rs15102485
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Aircraft-LBDet: Multi-Task Aircraft Detection with Landmark and Bounding Box Detection

Abstract: With the rapid development of artificial intelligence and computer vision, deep learning has become widely used for aircraft detection. However, aircraft detection is still a challenging task due to the small target size and dense arrangement of aircraft and the complex backgrounds in remote sensing images. Existing remote sensing aircraft detection methods were mainly designed based on algorithms employed in general object detection methods. However, these methods either tend to ignore the key structure and s… Show more

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Cited by 5 publications
(2 citation statements)
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“…Object detection aims to locate and classify objects within images. Significant advancements in deep learning have established a robust foundation for its application in diverse fields such as intelligent driving vehicles [21], medical healthcare [22], agricultural robots [23], and remote sensing [24][25][26]. Currently, object detection methods are mainly categorized into two-stage and one-stage methods.…”
Section: Related Workmentioning
confidence: 99%
“…Object detection aims to locate and classify objects within images. Significant advancements in deep learning have established a robust foundation for its application in diverse fields such as intelligent driving vehicles [21], medical healthcare [22], agricultural robots [23], and remote sensing [24][25][26]. Currently, object detection methods are mainly categorized into two-stage and one-stage methods.…”
Section: Related Workmentioning
confidence: 99%
“…Data enhancement techniques can alleviate this problem, but they cannot eliminate it. Ma et al [22] expanded the dataset utilizing flipping, rotating, and random clipping to create suitable training conditions for the model. Song et al [23] used translation and flip to enrich the features of the aircraft dataset, hoping that the model could cope with different detection angles.…”
Section: Introductionmentioning
confidence: 99%