ASFM-AFD: Multimodal Fusion of AFD-Optimized LiDAR and Camera Data for Paper Defect Detection

Current paper defect detection methods often struggle to satisfy the critical demands for precision and adaptability in complex industrial environments. This study developed a novel multisensor platform combining low-precision light detection and ranging (LiDAR) and high-resolution camera, along with an adaptive sensor fusion module-adaptive Fourier decomposition (ASFM-AFD) framework for processing multimodal data. This integration substantially improves defect detection capabilities. The AFD-based optimization approach effectively enhances the quality of low-precision LiDAR data, achieving performance comparable to high-precision LiDAR while providing cost-effective solution for industrial inspection applications. Comparative studies with empirical mode decomposition (EMD) and variational mode decomposition (VMD) demonstrate that AFD exhibits superior noise reduction and resolution enhancement while maintaining stable performance under various challenging conditions. Experimental results show that the proposed framework achieves an average precision of 97.5% for paper defect detection in high-speed production environments, significantly improving detection accuracy and demonstrating robustness across various operating conditions.