Computer vision applications in the mining industry: a systematic review

Computer vision (CV) is a highly-relevant field within the artificial intelligence that enables machines to interpret and analyse visual data from the physical world, mimicking human vision. In the mining industry, CV applications have become popular as a means to address critical challenges such as optimizing resource extraction, improving safety, and minimizing environmental impact. However, existing research often addresses specific use cases, resulting in a fragmented understanding of CV’s broader applications across the sector. Based on the PRISMA 2020 guidelines, this systematic literature review (SLR) addresses this gap by consolidating diverse research into a broad resource. Peer-reviewed journal articles (2019–2024) from Web of Science and Scopus were reviewed, with 101 studies meeting inclusion criteria. The findings indicate that CV technologies have evolved from isolated tools to interconnected analytical ecosystems spanning the entire mining value chain. Applications range from safety monitoring and ore detection to environmental assessments, enhanced by multi-sensor fusion and deep learning. However, a critical validation gap emerges: while safety rhetoric dominates adoption justifications, fewer than 25% of studies provide quantitative validation. Environmental robustness remains the primary technical barrier, with methodological fragmentation undermining deployment confidence. The field requires a shift toward comprehensive field validation, standardized frameworks, and responsible deployment. This transformation signals the emergence of predictive, environmentally conscious mining practices that will fundamentally reshape resource extraction while advancing CV capabilities for sustainable industrial applications.