QYOLO: Lightweight Object Detection via Quantum Inspired Shared Channel Mixing
The rapid advancement of object detection architectures has positioned single stage detectors as the dominant solution for real-time visual perception. A primary source of computational overhead in these models lies in the deep backbone stages, where C2f bottleneck modules at high stride levels accumulate a disproportionate share of parameters due to quadratic scaling with channel width. This work introduces QYOLO, a quantum-inspired channel mixing framework that achieves genuine architectural compression by replacing the two deepest backbone C2f modules at P4/16 (512 channels) and P5/32 (1024 channels) with a compact QMixBlock. The proposed block performs global channel recalibration thr...
| Authors | Garvit Kumar Mittal, Sahil Tomar, Sandeep Kumar |
|---|---|
| Primary category | cs.CV |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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