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International Conference on Communication, Computing and Electronics Systems (ICCCES)

Quantum-Assisted Faster RCNN Detection and Classification of Small Underwater Objects Using a Hybrid Classical-Quantum Models

Dharshan V, S Markkandan, A. Sugunapriya

Published Jan 21, 2026• 0 citations
Publisher

Abstract

Underwater Small object detection and classification are crucial for ecological monitoring, yet traditional algorithms struggle with underwater image complexity, including low visibility and considerable fluctuations in object sizes. This article presents Quantum-Assisted Faster R-CNN Detection and Classification Models specifically suited to meet these problems, leveraging a transfer learning strategy between classical and quantum neural networks. Firstly, standard models like ResNet-50, EfficientNet-B0, and MobileNet-V2 are pretrained for feature extraction on the underwater dataset. Secondly, these models are then integrated with a quantum layer with two entangling layers, giving three Quantum Neural Network (QNN) hybrid architectures within the Faster R-CNN framework. Finally, the UTDAC2020 dataset is used to evaluate the models, having four underwater small object classes (echinus, starfish, holothurian, and scallop) with images standardized to$640 \times 640$pixels to account for variations in resolution and class imbalance. The QNN-ResNet-50 model performs better than other hybrid models, with a validation accuracy of 81.09% and an F1 score of 0.8347. This work demonstrates the possibility of hybrid classical-quantum models as robust solutions for underwater small object detection and classification, paving the way for future advances in demanding visual environments.

DOI
10.1109/iccces62661.2026.11436387

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