During natural evolution, the primary visual cortex (V1) in lower mammals typically exhibits salt-and-pepper organizations, whereas higher mammals and primates develop pinwheel structures with distinct topological properties. Although V1 neurons are generally believed to act as edge detectors, the functional advantages of pinwheel structures over salt-and-pepper organizations remain underexplored. To address this, we propose a two-dimensional self-evolving spiking neural network that integrates Hebbian-like plasticity rules and empirical morphological data. By presenting the network to extensive image datasets, it evolves from salt-and-peppers to pinwheels, with neurons becoming localized bandpass filters tuned to various orientations. This transformation increases visual field overlap and enhances functional organization. Our findings reveal that neurons at pinwheel centers (PCs) respond more rapidly to complex spatial textures in natural images compared to those in salt-and-pepper configurations. PCs act as first-order stage processors with heightened sensitivity and reduced latency to intricate contours, while surrounding iso-orientation domains refine edge representations for enhanced perceptual clarity. This study presents the first theoretical evidence that pinwheel structures function as crucial detectors of spatial contour saliency in the primary visual cortex.