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In our literature review, we observe that existing mosquito detection approaches mainly rely on acoustic-only, vision-only, or audiovisual methods, often exhibiting limited robustness in real-world conditions. Many studies employ complex fusion strategies or computationally intensive models, such as Transformers or multi-sensor systems, leading to high latency and poor suitability for edge deployment, while others focus exclusively on visual modalities and neglect the robustness provided by acoustic information under low-light or noisy environments. In contrast, our work introduces a lightweight multimodal architecture that combines an acoustic CNN with an RGB–IR ResNet-based visual branch through intermediate fusion, explicitly addressing synchronization, computational efficiency, and robustness requirements for Edge AI deployment in real-world conditions. |
IEEE/ICACT20260085 Slide.09
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