Process-Aware Feature Modulation for Fine-Grained Connector Detection

Abstract

While deep learning has significantly improved the accuracy of visual detection systems, the integration of process semantics with visual perception for industrial assembly tasks remains largely unexplored. This study aims to develop a high-accuracy cable connector detection framework that incorporates process knowledge to enhance feature discrimination under varying industrial imaging conditions. To achieve this goal, we build an enhanced Fully Convolutional One-Stage (FCOS) detector with a ConvNeXt V2 backbone and introduce a Process Feature Linear Modulation (PFNM) module. The proposed module adaptively modulates visual features using encoded process semantics, enabling the detector to align visual perception with assembly logic. Experiments conducted on an industrial connector dataset demonstrate that the proposed method achieves an mAP of 84.7%, outperforming representative state-of-the-art detectors including YOLOv11, RT-DETR, and DINO while maintaining an inference speed of 17.6 FPS. Ablation studies further show that each component contributes to progressive performance improvement, and the complete framework achieves a 5.5% AP gain over the ResNet-50 baseline. These results indicate that integrating process knowledge with visual feature learning effectively improves feature discrimination and provides a promising paradigm for process-aware perception in intelligent manufacturing.