SIRST-UAVB: Single frame infrared small target dataset - UAV and birds.

Citation Author(s):
Jiangnan
Yang
Shuangli
Liu
Jingjun
Wu
Xueli
Huang
Submitted by:
JiangNan Yang
Last updated:
Mon, 12/23/2024 - 21:07
DOI:
10.21227/7ey9-fb32
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License:
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Abstract 

Infrared dim-small target detection has gained increasing importance in both military and civilian applications due to its ability to detect thermal radiation, operate effectively at night, passively sense radiation, and offer strong concealment with high resistance to interference. These capabilities make it ideal for systems such as aircraft and bird surveillance, missile guidance, and maritime rescue operations. In these applications, the need for mid- to long-range observations often results in small targets that appear dim and are difficult to detect. This dataset, named SIRST-UAVB, provides infrared images captured in the 3–5 μm wavelength range using a mid-wave infrared camera, with a resolution of 640×512 pixels and shooting distances ranging from 100 to 800 meters. The dataset predominantly features small targets, which make up 94.3% of the total data and include unmanned aerial vehicles (UAVs) and birds. These targets are presented against complex backgrounds, such as skies, clouds, buildings, and vegetation, which introduce significant challenges. The combination of low signal-to-noise ratio (SNR), low signal-to-clutter ratio (SCR), and substantial background noise further complicates detection, as targets are easily obscured by environmental clutter.

Instructions: 

The SIRST-UAVB dataset consists of 2,400 training images and 600 testing images. The annotations include YOLO format *.txt files for bounding box detection tasks and *.jpg mask labels for segmentation tasks.

Funding Agency: 
Southwest University of Science and Technology
Grant Number: 
NO.20zx7120