 Infrared small target detection is a critical technology in both military and civilian applications, such as surveillance, security, defense, and combat. It is difficult to accurately detect small targets in real time due to their small size and similarities in gray level and texture with the surrounding environment, as well as interference from infrared imaging systems in UAVs. To address these issues, we propose a weighted local contrast method, WRDLCM, based on the contrast mechanism of the human visual system. The method combines contrast ratios at the pixel level with an improved regional intensity level to establish a weight function that suppresses complex backgrounds and random noise. Finally, the contrast and weight functions are combined to create WRDLCM, which enhances the target while suppressing background interference. Our algorithm outperformed other methods in terms of ROC curve, signal-to-noise ratio, and bit error rate. Furthermore, it is suitable for small sample sizes and can be implemented on FPGA hardware. This article was authored by Yuan Yuanqian, Huichin Wang, Yupeng, and others. We are article.tv, links in the description below.