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New Quantum-Classical Model Enhances Sonar Object Detection with Fewer Parameters

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Quantum-Gated LiteSSD, a novel hybrid quantum-classical framework for object detection using forward-looking sonar. This model significantly reduces the number of parameters compared to existing detectors like YOLO26s and SSD-VGG16, while maintaining competitive accuracy on benchmark datasets. The framework reformulates quantum processing as a channel-gating mechanism for spatial feature modulation, making it suitable for deployment on embedded systems. AI

IMPACT This research could enable more efficient underwater perception systems on resource-constrained devices.

RANK_REASON The item is a research paper detailing a new model and its experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Quantum-Classical Model Enhances Sonar Object Detection with Fewer Parameters

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The item is a research paper detailing a new model and its experimental results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Niloy Kumar Mondal, Poulomi Sarker Puja ·

    Quantum-Gated LiteSSD: A Parameter-Efficient Lightweight Hybrid Quantum-Classical Framework for Forward-Looking Sonar Object Detection

    arXiv:2609.14025v1 Announce Type: new Abstract: Forward-looking sonar object detection is essential for underwater perception, yet deployment on embedded platforms requires highly compact models. To address this challenge, we explore quantum computing and introduce Quantum-Gated …

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