VisText-Mosquito: A Multimodal Dataset and Benchmark for AI-Based
  Mosquito Breeding Site Detection and Reasoning
Authors: name: Md. Adnanul Islam, name: Md. Faiyaz Abdullah Sayeedi, name: Md. Asaduzzaman Shuvo, name: Muhammad Ziaur Rahman, name: Shahanur Rahman Bappy, name: Raiyan Rahman, name: Swakkhar Shatabda

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, our fine-tuned BLIP model achieves
a final loss of 0.0028, with a BLEU score of 54.7, BERTScore of 0.91, and
ROUGE-L of 0.87. 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