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Small object detection underpins wide-area vision tasks such as UAV and remote-sensing imagery. However, standard convolutions often exhibit low-pass smoothing behavior, which suppresses the sparse edge cues of tiny targets and may cause them to be overwhelmed by background clutter, leading to semantic information loss at the early stages of feature extraction. Inspired by the dual-fovea physiology of the eagle eye, we propose EagleConv, a bio-inspired operator that superimposes three Gaussian components into a center-excitation, surround-inhibition, peripheral-context response profile to selectively amplify small-object signals while attenuating noise. Architecturally, EagleConv adopts a sparse dual-pathway design that integrates partial-channel depthwise dual-foveated filtering, pointwise channel mixing, and adaptive residual gating, followed by anti-aliasing downsampling for robust dimensionality reduction. Experiments on three public benchmarks show that augmenting YOLOv11s with EagleConv delivers consistent and significant gains over multiple convolution-augmentation baselines, achieving state-of-the-art performance and confirming its transferability and generality as a plug-and-play module for wide-area small-object detection.