Quantum-Gated LiteSSD：用于前视声呐目标检测的参数高效轻量级混合量子-经典框架
Quantum-Gated LiteSSD: A Parameter-Efficient Lightweight Hybrid Quantum-Classical Framework for Forward-Looking Sonar Object Detection
- Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）
- Khulna University of Engineering & Technology（库尔纳工程技术大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出Quantum-Gated LiteSSD，一种参数高效的混合量子-经典前视声呐检测框架，通过量子启发的通道门控机制在极低参数下实现高精度目标检测。
AI中文摘要：
前视声呐目标检测对于水下感知至关重要，但在嵌入式平台上部署需要高度紧凑的模型。为应对这一挑战，我们探索量子计算并引入Quantum-Gated LiteSSD，一种参数高效的混合量子-经典检测器，它将QuCNet风格的多电路量子处理重构为以恒等为中心的通道门控机制，用于空间特征调制。在Marine Debris Watertank数据集和UATD前视声呐基准上的实验表明，该检测器实现了有效的参数-精度权衡。所提出的检测器在Watertank上达到90.84%的mAP50，参数数量比YOLO26s减少约62倍，比SSD-VGG16减少164.3倍。在UATD上，该模型仅用0.150M参数即达到70.37%的mAP50，比SSGA-YOLO小约4.1倍，同时保留了有意义的的多类检测能力。
英文摘要：
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 LiteSSD, a parameter-efficient hybrid quantum--classical detector that reformulates QuCNet-style multi-circuit quantum processing as an identity-centered channel-gating mechanism for spatial feature modulation. Experiments on the Marine Debris Watertank dataset and UATD forward-looking sonar benchmarks demonstrate an effective parameter--accuracy trade-off. The proposed detector achieves 90.84% $\mathrm{mAP}_{50}$ on Watertank with approximately $62\times$ fewer parameters than YOLO26s and $164.3\times$ fewer parameters than SSD-VGG16. On UATD, the model achieves 70.37% $\mathrm{mAP}_{50}$ with only 0.150M parameters, making it approximately $4.1\times$ smaller than SSGA-YOLO while retaining meaningful multi-class detection capability.
