A multi-cognitive PCB defect detection model integrating Mamba

Printed Circuit Boards (PCBs) pose significant challenges for defect detection due to their complex textures, small defect targets, and subtle inter-class similarities. Traditional inspection methods are limited in robustness, while many deep learning-based detectors struggle with insufficient tiny-target feature extraction, low feature utilization, and high model complexity. To address these limitations, this study introduces PCB-MMF, a multi-cognitive hybrid framework integrating the Mamba state space model. The proposed MM-NET backbone combines CNN-based local feature extraction with Mamba-based global modeling, augmented by a Three-Stage Multi-Receptive Module (TSMR) to fuse global and multi-scale features while mitigating redundancy. A Multi-Cognitive Visual Augmentation Module (MC-VAM) enhances attention to critical regions and preserves shallow features through residual connections, while a Lightweight Group-Shared Detection Head (LGSD) applies parameter sharing to reduce computational cost without compromising accuracy. Experimental results on HRIPCB, DeepPCB, and DsPCBSD+ datasets demonstrate that PCB-MMF achieves mAP50 scores of 93.43%, 98.68%, and 85.39%, respectively. Furthermore, additional generalization experiments on the NEU-DET dataset achieve an mAP50 of 76.69%, confirming the robust performance of PCB-MMF across different industrial scenarios. Compared with the YOLO11 model, PCB-MMF reduces the number of parameters by 8.9% (from 2.58 to 2.35M) and the computational load (FLOPs) by 12.70%. Relative to the Mamba-YOLO model, PCB-MMF reduces the number of parameters by 58.48% and the computational load (FLOPs) by 55.28%. These findings confirm that PCB-MMF offers a favorable balance between accuracy and efficiency, providing a promising solution for lightweight, high-precision PCB defect detection in industrial applications.