Fast and accurate object classification has attracted significant attention in numerous applications. Traditional imaging-based methods often face challenges due to high computational loads and data redundancy, which limit real-time performance. Recent research has demonstrated the potential of non-imaging techniques that combine invariant features with single-pixel detection. This paper proposes an optimized modulation strategy based on Hu invariant, reducing the required number of illumination patterns from five to three while maintaining classification accuracy. The approach achieves a 67% increase in update rate, reaching a theoretical frequency of 7.4 kHz. We performed recognition on digit images from the MNIST dataset, and simulation results showed that the proposed method attained a recognition accuracy above 90%, comparable to traditional methods. The proposed method offers a practical solution with potential applications in optical target classification, dynamic light field analysis, and real-time industrial inspection.
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