Camouflaged object detection (COD) methods suffer severe degradation on small camouflaged objects and remain incapable of real-time inference on edge devices. We propose LeanCOD, a framework that pairs a strong foundation-model encoder with a lightweight decoder to enable high-resolution inference. A size-aware composite loss further strengthens supervision on small camouflaged objects. Our size-wise experiments reveal that the 0–1% extra-small-object regime is the major performance bottleneck for existing COD methods. LeanCOD achieves an $S_{\alpha }$ of 0.915 on COD10K at a $576\times 576$ resolution, outperforming competing methods at equal or lower resolutions. Deployed with TensorRT FP16 on an NVIDIA Jetson AGX Orin, LeanCOD runs at 31.6 FPS while maintaining an $S_{\alpha }$ of 0.908 at a $576\times 576$ resolution, exceeding the 30 FPS real-time threshold.