Structural variants are variants in the genome that span 50 bases or more. Advances in long read sequencing make it increasingly possible to investigate structural variant diversity in large cohorts. In many non-model species, differentiating between true and erroneous structural variant calls is difficult due to the lack of a truth set. This project aims at establishing a workflow to obtain a high-confidence set of structural variant genotypes in cattle which does not rely on a truth set of validated variants. We aligned PacBio HiFi reads from 49 cattle that had at least 15x coverage to the Bos taurus ARS-UCD2.0 reference genome to identify structural variants using Sniffles2, Sawfish, LongcallD and SVision-pro. A set of increased confidence structural variants was constructed by standard filtering of variants with low quality scores, removing missing genotypes, removing consensus lengths below 45 bp, and using intersection datasets between the callers. Additionally, genomic areas with significantly outlying coverage values were included in a 'blacklist' of coordinates from which to exclude structural variant calls. Results show that the structural variants highlighted by the latter method overlap largely with structural variants filtered out by the set of initial filters but exclude an additional 200-500 structural variants depending on the caller. These findings suggest that incorporating coverage-based exclusion criteria can improve the reliability of structural variant datasets in the absence of validated truth sets.