VisText-Mosquito: A Unified Multimodal Benchmark Dataset for Visual Detection, Segmentation, and Textual Reasoning on Mosquito Breeding Sites
This work addresses a global health issue by enabling AI-based proactive control of mosquito breeding sites, though it is incremental as it applies existing methods to a new dataset.
The paper tackles the problem of mosquito-borne diseases by introducing VisText-Mosquito, a multimodal dataset for visual detection, segmentation, and textual reasoning on mosquito breeding sites, with models achieving high precision (e.g., 0.92926 for detection) and strong reasoning scores (e.g., BLEU 54.7).
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 reasoning for mosquito breeding site analysis. The dataset includes 1,828 annotated images for object detection, 142 images for water surface segmentation, and natural language reasoning 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 reasoning 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: https://github.com/adnanul-islam-jisun/VisText-Mosquito