Hierarchical Region-Guided Multi-Task Network for Energy Label Quality Inspection

Automated energy efficiency label quality inspection differs from traditional single-defect detection, as it requires joint analysis of multiple related targets, including product regions, energy efficiency labels, surface defects, energy grades, and label placement relationships. These targets exhibit complex spatial dependencies, requiring both fine-grained local perception and hierarchical spatial reasoning. To address this challenge, we propose a Hierarchical Region-Guided Multi-Task Network (HRG-MTN) for energy efficiency label quality inspection. HRG-MTN first extracts shared multi-scale visual features and introduces a Hierarchical Region Localization Module (HRLM) to progressively locate the product region, energy label region, and grade region through scale-aware feature aggregation. Based on the localized regions, Task-oriented Quality Inspection Branches (TQIB) perform three complementary tasks, including geometric position deviation estimation, fine-grained defect detection, and energy grade recognition. Furthermore, a Multi-task Structural Consistency Loss is designed to optimize task-specific predictions while preserving spatial consistency among hierarchical regions. To facilitate comprehensive evaluation, we construct the PQI dataset containing 3892 images, including 3512 training samples and 380 testing samples, covering diverse package quality inspection scenarios. Experimental results show that HRG-MTN achieves 99.3% precision, 99.7% recall, 99.5% F1-score, and 75.6% mAP0.5:0.95 for defect inspection, improving the mAP0.5:0.95 by 1.4% points compared with the ShortcutBreaker method.