An Infrared Small Target Detection Method Based on a Weighted Human Visual Comparison Mechanism for Safety Monitoring

Abstract

Infrared small target detection is a crucial technology in both military and civilian applications, including surveillance, security, defense, and combat. However, accurate infrared detection of small targets in real-time is challenging due to their small size and similarity in gray level and texture with the surrounding environment, as well as interference from the infrared imaging systems in unmanned aerial vehicles (UAVs). This article proposes a weighted local contrast method based on the contrast mechanism of the human visual system. Initially, a combined contrast ratio is defined that stems from the pixel-level divergence between the target and its neighboring pixels. Then, an improved regional intensity level is used to establish a weight function with the concept of ratio difference combination, which can effectively suppress complex backgrounds and random noise. In the final step, the contrast and weight functions are combined to create the final weighted local contrast method (WRDLCM). This method does not require any preconditioning and can enhance the target while suppressing background interference. Additionally, it is capable of detecting small targets even when their scale changes. In the experimental section, our algorithm was compared with some popular methods, and the experimental findings indicated that our method showed strong detection capability based on the commonly used performance indicators of the ROC curve, SCRG, and BSF, especially in low signal-to-noise ratio situations. In addition, unlike deep learning, this method is appropriate for small sample sizes and is easy to implement on FPGA hardware.