A novel hybrid quantum dilated convolutional Kronecker network (QDCKN) for marine object detection and classification

Locating and identifying underwater bodies is challenging because underwater images are often low-luminance, turbid, and have ambiguous backgrounds. Object detection and computer vision technologies are gaining popularity in marine biology for the quick and efficient detection, and processing in the aquatic environment, despite these drawbacks. To overcome these limitations, a novel hybrid framework, Quantum (Conceptual) Dilated Convolutional Kronecker Networks (QDCKN) is proposed that integrates quantum-inspired dilated convolutions, fuzzy uncertainty modeling, and Kronecker-structured feature compression in a mathematically coupled architecture. The proposed framework follows a integrated two-stage underwater object analysis framework consisting of Savitzky-Golay filtering for image pre-processing and YOLOv3 for object detection followed by a QDCNN-Fuzzy Deep Kronecker Network (DKN) module for region-wise classification. Unlike conventional approaches that treat feature extraction and classification independently, the proposed method establishes a cohesive interaction between quantum-inspired feature representation, fuzzy logic-based uncertainty handling, and structured feature compression, enabling robust recognition under non-uniform illumination, occlusions, and noise. An experimental evaluation conducted within the evaluated dataset (Underwater Object Detection dataset) demonstrates that QDCKN achieves a maximum classification accuracy of 93.40%, precision of 92.20%, and recall of 94.60%, outperforming SA-FPN, MarineDet, mResNet, and EDR. The experimental analysis and ablation study proves that the proposed work contributes to improved performance and stability under challenging underwater conditions within the evaluated dataset.