A Flexible Multitask Framework With Decoupled Training Paradigm for Radiographic Weld Images

Weld sign detection (WSD), weld edge segmentation (WES), and weld defect detection (WDD) are three major tasks in artificial intelligence-empowered radiographic weld images. Most of the existing methods focus on improving detection performance on a single task; the others focus on two tasks and currently still lack a more comprehensive detection method. We argue that simultaneously accomplishing three tasks can not only efficiently utilize the multitask measurement data for downstream industrial applications but also enhance the resource utilization of industrial deployment. To this end, we propose a flexible multitask framework for effectively addressing three tasks. First, a multitask framework is elegantly designed, where the shared features are used as a flexible interface for connecting the shared feature encoder (SFE) with multiple task-specific prediction heads. Particularly, a two-stage decoupled training method is proposed to improve the multitask method’s accuracy by separately pretraining the SFE and fine-tuning the task-specific prediction heads. We instantiate a multitask training-decoupled network (MT-TDN) on an open-source dataset GDXray and a real-world dataset private practical pipeline weld dataset (PPWD). The experimental results demonstrate that the proposed method achieves a higher balance of accuracy and efficiency on three tasks, including WDD (0.959 mAP), WES (0.941 mAP), and WSD (0.972 mAP).