Seeing Clearly: How Tech Boosts Tiny Object Detection in Blurry Infrared Pics

Infrared (IR) imaging is a big deal. It helps in many areas like spotting objects, checking factories, and even medical scans. But there's a catch: IR images are often blurry and noisy. This makes it tough for regular object detection models to work well. To tackle this, a new method has been developed. It combines two key technologies: super-resolution and an improved YOLOv8 model. The first part, called LightweightSRNet, makes blurry IR images clearer without using too much computer power. The second part, called HG-MHA, helps the model focus on the important stuff and ignore the noise. Another cool feature is the SC-BiFPN module. It mixes different levels of details to make small objects easier to spot. Plus, there's a C2f-Ghost-Sobel module. It helps detect edges and details quickly, making the whole process faster. Tests on the HIT-UAV dataset showed great results. The recall rate went up from 70. 23% to 80. 51%, and the mean average precision (mAP) improved from 77. 48% to 83. 32%. This means the model is much better at finding small objects in IR images. This new method could be a game-changer for real-world applications. It makes IR imaging more reliable and efficient. The best part? The code and datasets used in this study are available for anyone to check out and use.