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2026-10-20, Week 43 |
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* Edit or answer any Q&A by selecting Q&A number Hyper Link below + Write button (Save)
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ICACT20260085 Question.1
Questioner: taherul203158@gmail.com 2026-02-10 오후 3:52:39 |
ICACT20260085 Answer.1
Answer by Auhor smkara.samb@univ-thies.sn 2026-02-10 오후 3:52:39
Chrome  Click!! |
| What are the main limitations of using only acoustic or only visual methods for mosquito detection, and how does the proposed multimodal fusion approach address these limitations? |
Using a single modality has clear drawbacks: acoustic-only methods are highly sensitive to ambient noise, which degrades detection accuracy, while visual-only methods suffer in low-light, nighttime, or occluded conditions where mosquitoes are difficult to observe. The proposed acoustic–visual multimodal fusion overcomes these limitations by leveraging the complementarity of both modalities—acoustics compensates for poor visibility, and vision (RGB+IR) mitigates noisy audio—through intermediate feature-level fusion, resulting in significantly improved robustness and accuracy across day/night, low-light, and high-noise environments compared to unimodal approaches |
ICACT20260085 Question.2
Questioner: taherul203158@gmail.com 2026-02-10 오후 3:55:03 |
ICACT20260085 Answer.2
Answer by Auhor smkara.samb@univ-thies.sn 2026-02-10 오후 3:55:03
Chrome Click!! |
| How does the intermediate fusion of acoustic and visual data work in the proposed model, and what advantages does it offer over early or late fusion strategies? |
In our work, we show that unimodal approaches suffer from important limitations: acoustic-only methods are highly sensitive to ambient noise, while vision-only methods (even RGB+IR) experience significant performance degradation in low-light, nighttime, or occluded conditions. To address these issues, we adopt an intermediate audio–visual fusion strategy in which high-level features are extracted separately from acoustic spectrograms using a lightweight CNN and from RGB+IR images using a lightweight ResNet-18, and then concatenated before the final classification stage. This approach preserves the complementarity of both modalities and exploits their joint representations more effectively than early fusion, which is often affected by heterogeneous raw data, or late fusion, which combines independent decisions, resulting in higher robustness, improved accuracy, and compatibility with Edge AI constraints |
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