The ongoing need to sustainably manage fishery resources can benefit from fishery-independent monitoring of fish stocks. Camera systems, particularly baited remote underwater video system (BRUVS), are a widely used and repeatable method for monitoring relative abundance, required for building stock assessment models. The potential for BRUVS-based monitoring is restricted, however, by the substantial costs of manual data extraction from videos. Computer vision, in particular deep learning (DL) models, are increasingly being used to automatically detect and count fish at low abundances in videos. One of the advantages of BRUVS is that bait attractants help to reliably detect species in relatively short deployments (e.g., 1 h). The high abundances of fish attracted to BRUVS, however, make computer vision more difficult, because fish often obscure other fish. We build upon existing DL methods for identifying and counting a target fisheries species across a wide range of fish abundances. Using BRUVS imagery targeting a recovering fishery species, Australasian snapper (Chrysophrys auratus), we tested combinations of three further mathematical steps likely to generate accurate, efficient automation: (1) varying confidence thresholds (CTs), (2) on/off use of sequential non-maximum suppression (Seq-NMS), and (3) statistical correction equations. Output from the DL model was more accurate at low abundances of snapper than at higher abundances (>15 fish per frame) where the model over-predicted counts by as much as 50%. The procedure providing the most accurate counts across all fish abundances, with counts either correct or within 1–2 of manual counts (R2 = 88%), used Seq-NMS, a 45% CT, and a cubic polynomial corrective equation. The optimised modelling provides an automated procedure offering an effective and efficient method for accurately identifying and counting snapper in the BRUV footage on which it was tested. Additional evaluation will be required to test and refine the procedure so that automated counts of snapper are accurate in the survey region over time, and to determine the applicability to other regions within the distributional range of this species. For monitoring stocks of fishery species more generally, the specific equations will differ but the procedure demonstrated here could help to increase the usefulness of BRUVS.