VisText-Mosquito: A Unified Multimodal Dataset for Visual Detection, Segmentation, and Textual Explanation on Mosquito Breeding Sites

[2506.14629] VisText-Mosquito: A Unified Multimodal Dataset for Visual Detection, Segmentation, and Textual Explanation on Mosquito Breeding Sites Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2506.14629 (cs) [Submitted on 17 Jun 2025 ( v1 ), last revised 11 Apr 2026 (this version, v3)] Title: VisText-Mosquito: A Unified Multimodal Dataset for Visual Detection, Segmentation, and Textual Explanation on Mosquito Breeding Sites Authors: Md. Adnanul Islam , Md. Faiyaz Abdullah Sayeedi , Md. Asaduzzaman Shuvo , Shahanur Rahman Bappy , Md Asiful Islam , Swakkhar Shatabda View a PDF of the paper titled VisText-Mosquito: A Unified Multimodal Dataset for Visual Detection, Segmentation, and Textual Explanation on Mosquito Breeding Sites, by Md. Adnanul Islam and 5 other authors View PDF HTML (experimental) Abstract: Mosquito-borne diseases pose a major global health risk, requiring early detection and proactive control of breeding sites to prevent outbreaks. In this paper, we present VisText-Mosquito, a multimodal dataset that integrates visual and textual data to support automated detection, segmentation, and explanation for mosquito breeding site analysis. The dataset includes 1,828 annotated images for object detection, 142 images for water surface segmentation, and natural language explanation texts linked to each image. The YOLOv9s model achieves the highest precision of 0.92926 and mAP@50 of 0.92891 for object detection, while YOLOv11n-Seg reaches a segmentation precision of 0.91587 and mAP@50 of 0.79795. For textual explanation generation, we tested a range of large vision-language models (LVLMs) in both zero-shot and few-shot settings. Our fine-tuned Mosquito-LLaMA3-8B model achieved the best results, with a final loss of 0.0028, a BLEU score of 54.7, BERTScore of 0.91, and ROUGE-L of 0.85. This dataset and model framework emphasize the theme "Prevention is Better than Cure", showcasing how AI-based detection can proactively address mosquito-borne disease risks. The dataset and implementation code are publicly available at GitHub: this https URL Comments: Accepted at CVPRW 2026 Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Computation and Language (cs.CL) Cite as: arXiv:2506.14629 [cs.CV] (or arXiv:2506.14629v3 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2506.14629 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Md. Faiyaz Abdullah Sayeedi [ view email ] [v1] Tue, 17 Jun 2025 15:24:30 UTC (2,512 KB) [v2] Sat, 20 Sep 2025 12:30:04 UTC (3,605 KB) [v3] Sat, 11 Apr 2026 19:43:40 UTC (3,869 KB) Full-text links: Access Paper: View a PDF of the paper titled VisText-Mosquito: A Unified Multimodal Dataset for Visual Detection, Segmentation, and Textual Explanation on Mosquito Breeding Sites, by Md. Adnanul Islam and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2025-06 Change to browse by: cs cs.CL References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Dual-Modal Chicken Mortality Detection Using Dynamic Hybrid Convolution-Based Feature Fusion.#9346
• SALC-Net: A lightweight contour-preserving segmentation network for yak body segmentation in complex grazing environments.#430711
• Lewis_et_al_2026_wetland_fish_data#325700
• Night raider : use your net.#1121835
• Enhancing Aedes aegypti breeding site detection: A deep learning approach to tire identification in unmanned aerial vehicle imagery from urban Indonesia.#433834