IRA-YOLO: Inter-Class Relation Attention for PPE Detection in Industrial Scenes

Abstract

Personal protective equipment (PPE) detection in industrial scenes is a joint classification-and-localization problem complicated by partial occlusion, scale variation, and background clutter. PPE categories also follow structured physical relations; for example, helmets are normally associated with heads and gloves with hands. Existing attention modules mainly reweight appearance features and seldom encode these directional inter-class dependencies. We therefore propose IRA-YOLO, a YOLO-based object detector equipped with an Inter-Class Relation Attention Module (IRAM). The IRAM projects each multi-scale feature map into class-specific subspaces, learns asymmetric pairwise relations among category attention maps, and returns the relational prior to the detection feature through residual fusion. On SH17, IRA-YOLO improves mAP@50:95 from 0.396 to 0.411 over YOLOv11s while adding 0.42 M parameters and 0.99 GFLOPs, and it maintains 139.83 FPS in the single-image inference test. Cross-dataset evaluation on CHVG further supports the transferability of the proposed feature refinement. These results indicate that explicit inter-class relation modeling provides complementary semantic context for multi-class PPE detection.