A fast deep-space infrared multi-target detection algorithm based on clustering

This paper presents a fast real-time multi-target detection algorithm based on line target clustering. Adaptive threshold is applied to image segmentation; and then enclosing rectangle prosthetics, line target extraction and clustering merger are utilized for the binary image to implement full-field pixel-level targets detection and conduct ID tag. So undetected problems caused by the traditional detection algorithm can be avoid; Finally, a five points square predictor and cost function are constructed for trajectory matching, by which the problems of multi-target division, cross, temporarily lost due to overlap and so on are effectively resolved. The experiments are carried on SOPC hardware platform and the results show that the proposed algorithm can perform real-time detection accurately for the deep-space objects.