A Soldering Iron Safety State Detection Method Based on Instance-Level Interaction Understanding

In electronic training scenarios, the safety risk of a soldering iron cannot be determined by object detection alone, as its state must be further distinguished among hand-held, stand-supported, desk-exposed, and uncertain interactions. To address this problem, this paper proposes RISNet, the Relation-aware Interaction State Network, which establishes a two-stage instance-level interaction understanding framework for soldering iron safety monitoring. In the first stage, YOLO is used to generate candidate instances of soldering irons and related environmental objects, and dual-layer feature fusion is adopted to jointly exploit shallow details and deep semantics. In the second stage, the soldering iron is treated as the interaction subject. The Pointer-Head models associations between the subject and contextual objects, and the State-Head predicts the safety state conditioned on subject-object relational constraints. To reduce false alarms from false detections and weak interactions, RISNet introduces a Quality-Head that estimates the reliability of each interaction conclusion and filters low-quality predictions during inference. The unknown label is used during training as conservative supervision for weak, unreliable, or indeterminate interaction evidence, with semantics close to the no-interaction label in HOI. This paper also constructs the Soldering Iron Safety Interaction Dataset (SISID) to support detection, interaction modeling, and state evaluation of slender metallic tools in training scenarios. On the SISID validation split, RISNet achieves an Overall F1 of 95.38%, an Overall Precision of 96.73%, and an inference speed of 57.1 FPS, satisfying the centralized single-frame polling requirement considered in this work.