MCCE-Net: Multi-Modal Fusion Component Condition Evaluation Network in Neurosymbolic-Guided Electronics Remanufacturing

In electronics remanufacturing, product component condition detection is an essential step, of which the results can be further transformed into symbolic information to guide the robot in executing, amongst others, disassembly tasks. However, the accuracy of vision-based condition evaluation today is limited by the presence of complex backgrounds with textures and colors similar to the component. To address this challenge, a Multi-modal fusion Component Condition Evaluation Network (MCCE-Net) based on YOLOv11 is proposed to fuse the RGB and depth images to provide more robust feature information for object detection. To improve the cross-modality interaction, Feature Enhanced Attention (FEA) modules are integrated into the network. The network performances are validated on a collected multi-view dataset of laptops of different conditions with annotations for screw, screw_hole, missing_battery, and missing_cover. The presented results achieve an 85.0% mAP and outperform with 14.8% the vanilla YOLOv11. Experimental results also demonstrate that the MCCE-Net can achieve state-of-the-art performance on the collected dataset, greatly boosting neurosymbolic-based electronics remanufacturing.