Underwater imagery is increasingly used as an effective and repeatable method to monitor benthic ecosystems. Nevertheless, extracting ecologically relevant information from a large amount of raw images remains a time-consuming and somewhat laborious challenge. Thus, underwater imagery processing needs to strike a compromise between time-efficient image annotation and accuracy in quantifying benthic community composition. Designing and implementing robust image sampling and image annotation protocols are therefore critical to rationally address these trade-offs between ecological accuracy and processing time. The aim of this study was to develop and to optimize a reliable image scoring strategy based on the point count method using imagery data acquired on tide-swept macroepibenthic communities. Using a stepwise approach, we define an underwater imagery processing protocol that is effective in terms of (i) time allocated to overall image, (ii) reaching a satisfactory accuracy to estimate the occurrence of dominant benthic taxa, and (iii) adopting a sufficient taxonomic resolution to describe changes in community composition. We believe that our method is well adapted to investigate the composition of epibenthic communities on artificial reefs and can be useful in surveying colonization of other human structures (wind turbine foundations, pipelines, etc.) in coastal areas. Our strategy meets the increasing demand for inexpensive and time-effective tools for monitoring changes in benthic communities in a context of increasing coastal artificialization pressures.