VisText-Mosquito: A Multimodal Dataset and Benchmark for AI-Based
  Mosquito Breeding Site Detection and Reasoning
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
6852b60f10fbcda7a650e024
Md. Adnanul Islam
claimed_verified
2025-06-18T19:47:56.745Z
66ec02212eb421192b62c303
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/aw0TJjO3ohRlEYWlCzxQY.png
Md. Adnanul Islam
user
Adnanul
6852b60f10fbcda7a650e025
Md. Faiyaz Abdullah Sayeedi
claimed_verified
2025-06-18T16:15:25.634Z
667dcb41bc9abbfa3408382a
https://cdn-avatars.huggingface.co/v1/production/uploads/667dcb41bc9abbfa3408382a/U0b_xMDvZuzsErL-hm9tL.jpeg
Md. Faiyaz Abdullah Sayeedi
user
FaiyazAbdullah114708
6852b60f10fbcda7a650e026
Md. Asaduzzaman Shuvo
6852b60f10fbcda7a650e027
Muhammad Ziaur Rahman
6852b60f10fbcda7a650e028
Shahanur Rahman Bappy
6852b60f10fbcda7a650e029
Raiyan Rahman
6852b60f10fbcda7a650e02a
Swakkhar Shatabda
VisText-Mosquito: A Multimodal Dataset and Benchmark for AI-Based
  Mosquito Breeding Site Detection and Reasoning
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
VisText-Mosquito is a multimodal dataset combining visual and textual data for automated mosquito breeding site detection, segmentation, and reasoning, utilizing YOLOv9s, YOLOv11n-Seg, and a fine-tuned BLIP model.
YOLOv9s YOLOv11n-Seg BLIP model object detection water surface segmentation reasoning generation BLEU score BERTScore ROUGE-L