Improved Top-hat Transform-based Algorithm for Infrared Dim and Small Target Detection

The technology for detecting infrared dim and small targets in the sky background is relatively mature. However, detecting these targets in near-ground complex backgrounds poses challenges such as low accuracy, high false alarm rates, and poor real-time performance. To address these problems, a novel algorithm for detecting infrared dim and small targets based on an improved top-hat transform, referred to as OTHOLCM, is proposed in this study. The algorithm uses an image preprocessing method, OTH, based on an improved top-hat transformation to enhance the target and suppress the background. Different strategies are employed to process images with different gray values. Additionally, the algorithm uses an infrared dim and small target detection technique, OLCM, based on improved multi-scale local contrast. The OLCM uses target size characteristics to expand the target detection range while ensuring real-time performance. Experimental results show that the OTHOLCM algorithm can guarantee good real-time performance, improve target detection accuracy, and reduce the number of false alarms. Compared with advanced algorithms such as the three-layer template local difference measurement algorithm and the edge and corner awareness-based spatial-temporal tensor, the OTHOLCM algorithm increases the actual positive rate by almost 79% and 61%, respectively. In addition, it reduces the false positive rate by nearly 77% and 73%, respectively. Moreover, the target detection speed reaches 25 frames per second.