Adaptive feature expansion and fusion model for precast component segmentation

The assembly and production of sandwich panels for prefabricated components is crucial for the safety of modular construction. Although computer vision has been widely applied in production quality and safety monitoring, the large-scale differences among components and numerous background interference factors in sandwich panel prefabricated components pose substantial challenges. Therefore, maintaining the model recognition accuracy remains a big challenge in practical circumstances. This paper presents an instance segmentation model, namely adaptive feature expansion and fusion (AFFS). The proposed model includes a dynamic feature aggregation mechanism and possesses a flattened network architecture, enabling efficient feature processing and precise instance segmentation. Moreover, AFFS supports rapid adaptation to newly added data or component categories by updating only the feature extraction layers. Comprehensive experimental evaluations demonstrate that the proposed AFFS achieves outstanding recognition accuracy (mAP50 reaching 95.8% and mAPmin reaching 99.9%), significantly outperforming several state-of-the-art instance segmentation networks, including You Only Look Once (YOLO), Segmenting Objects by Locations v2 (SOLOv2), and Cascade Mask Region-based Convolutional Neural Network (Cascade Mask R-CNN).