LeanCOD: Real-Time Small Camouflaged Object Detection on Edge Devices

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 Sα of 0.915 on COD10K at a 576×576 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 Sα of 0.908 at a 576×576 resolution, exceeding the 30 FPS real-time threshold.