Deepseacap: a transformer approach for accurate and contextual captioning of underwater imagery

Low visibility, color distortion, and structural complexity are some of the harsh challenges that marine environments must confront. This research affords a transformational image caption structure, specifically designed for the assessment of underwater sceneries. To produce precise and significant captions for underwater images, the suggested technique combines seen-linguistic fusion, contextual and semantic enhancements, and hobby mechanisms. This proposed method consists of unique local language and spatial skills, enabling greater unique interpretation and context identity of complicated scenarios. The model achieves an increased accuracy of 91.40%, which extensively outperforms present techniques. The proposed technique consists of cutting-edge measures like Bilingual Evaluation Understudy (BLEU), Metric for Evaluation of Translation with Explicit ORdering (METEOR) and Consensus-based Image Description Evaluation (CIDEr). Contrast analysis and case studies show that the system may create captioning that is both aesthetically pleasing and linguistically rich, making it a valuable tool for tracking, exploration, and marine recording.