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 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% mAP_{50} on Watertank with approximately 62imes fewer parameters than YOLO26s and 164.3imes fewer parameters than SSD-VGG16. On UATD, the model achieves 70.37% mAP_{50} with only 0.150M parameters, making it approximately 4.1imes smaller than SSGA-YOLO while retaining meaningful multi-class detection capability.

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Source: arXiv cs.CV | 2026-09-15