Density-Frequency Dual-Feature Fusion for Intelligent Reel Spoke Recognition

The number and orientation of spokes are critical parameters for spray-path planning of cable reels. Aiming at the low efficiency of manual counting, contour fragmentation of slender structures caused by traditional edge detection, and the inability to automatically determine the spoke quantity, this paper proposes an automatic spoke-detection method based on radial density fusion and frequency-domain analysis. First, Hough-gradient-based circle transform is adopted to locate the outer-circle boundary, and a multi-feature fusion algorithm is utilized to accurately extract inner-circle parameters. Second, adaptive threshold segmentation is performed in the HSV color space according to the color statistics of the outer-circle region. Combined with ring mask and morphological closing operation, the complete spoke regions are extracted. Radial density projection is implemented within the annular region, where color density and edge confidence are weighted and fused to construct an angular-density curve. Fourier spectral analysis is carried out on the density curve, and the spoke number is automatically estimated via frequency-domain peak localization and harmonic-factor analysis. An energy function containing a data-fidelity term, a missed-detection penalty term and a structure-guidance term is constructed. The spoke-angle matching problem is transformed into an energy-minimization problem, and the optimal angles are obtained by traversal search in the offset-angle space. Experimental results demonstrate that the proposed method can effectively identify spokes of cable reels with different colors and different spoke quantities. Evaluated on 40 cable-reel images, the recognition success rate reaches 92.5%. Calculated against manually annotated center points, the mean pixel error of detected center coordinates is 14 pixels, and the mean actual deviation is 13.42 mm.