Open Access

ARTICLE

Helmet-Aware Identity-Consistent Tracking for Safe and Intelligent Power-System Operation and Maintenance

Lingwen Meng, Guobang Ban, Jintong Ma, Jiangang Liu, Mingyong Xin^*
Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
* Corresponding Author: Mingyong Xin. Email:

Energy Engineering https://doi.org/10.32604/ee.2026.085309

Received 08 May 2026; Accepted 14 July 2026; Published online 12 August 2026

Abstract

Safe and reliable operation of critical energy infrastructure requires continuous situational awareness of personnel during substation inspection, power-grid maintenance, and emergency repair. In these safety-critical power-system environments, identity-continuous worker trajectories can provide a perception basis for on-site safety supervision, operational-procedure review, emergency coordination, and post-incident reconstruction. In practice, workers are frequently observed under helmet-induced facial occlusion, uniform work clothing, equipment clutter, and temporary disappearance behind cabinets, protection panels, inspection vehicles, tools, or other personnel. These conditions make conventional face recognition, body-only re-identification, and short-term tracking unstable. This study develops a helmet-aware cross-modal identity-consistent tracking framework as an edge-deployable perception module for intelligent power-system operation and maintenance. The framework extracts face, body, helmet, and motion representations using dedicated branches; estimates the reliability of each modality from visibility, detection confidence, and temporal consistency; fuses the available cues through reliability-normalized attention; and maintains a memory bank for re-identification after occlusion. The helmet branch explicitly decomposes helmet observations into illumination-normalized color, structural shape, and learned visual descriptors, which enables protective equipment to serve as a stable auxiliary identity cue instead of being treated only as an occluder. The architecture is trained end to end using a unified objective that combines identity classification, batch-hard triplet learning, helmet-attribute supervision, temporal consistency, and association losses. Experiments are conducted on the custom PGW-Track-85309 dataset, which contains real videos from substation and power-grid maintenance scenarios together with controlled occlusion protocols. Compared with face-only, body-only, DeepSORT, ByteTrack, OC-SORT, BoT-SORT, and TransReID-based tracking baselines, the proposed framework reaches 90.4% identity F1 (IDF1), 91.8% multiple object tracking accuracy (MOTA), and 77.6% higher-order tracking accuracy (HOTA) on the held-out test set, while reducing identity switches to 11. The model runs at 43.8 frames per second (FPS) on an RTX 3090 GPU and 31.7 FPS on an NVIDIA Jetson Orin NX edge device, meeting the real-time requirement of 30 FPS. The results indicate that domain-aware helmet modeling, reliability-aware fusion, and memory-based association jointly improve identity continuity under realistic power-grid occlusion. By preserving personnel identity through equipment-induced occlusion and reappearance, the framework strengthens site-level safety awareness and supplies reliable trajectory evidence for intelligent operation-and-maintenance decision support, while retaining human review for safety-critical decisions.

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Keywords

Power-grid worker safety; multi-object tracking; person re-identification; helmet-aware feature learning; cross-modal fusion; occlusion robustness; edge deployment

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