As a common type of structural variation, an insertion refers to the addition of a DNA sequence into an individual genome and is usually associated with some inherited diseases. In recent years, many methods have been proposed for detecting insertions. However, the accurate calling of insertions is also a challenging task. In this study, we propose a novel insertion detection approach based on soft-clipped reads, which is called SIns. First, based on the alignments between paired reads and the reference genome, SIns extracts breakpoints from soft-clipped reads and determines insertion locations. The insert size information about paired reads is then further clustered to determine the genotype, and SIns subsequently adopts Minia to assemble the insertion sequences. Experimental results show that SIns can achieve better performance than other methods in terms of the F-score value for simulated and true datasets.