Generative AI-enhanced synthetic X-ray augmentation with gradient-based selection for battery detection in WEEE.

Automated detection of batteries in Waste Electrical and Electronic Equipment (WEEE) using X-ray imaging is critical for safe recycling, yet collecting large annotated real-world datasets remains prohibitively expensive and hazardous. This paper proposes a three-stage synthetic data pipeline to improve battery detection under limited labeled data conditions. First, dual-energy X-ray images are generated using physics-based ray-casting in Blender with automatic pixel-level annotation. Second, the synthetic-to-real domain gap is reduced using unpaired CycleGAN-based image translation. Finally, a class-conditional gradient-alignment criterion is introduced to rank synthetic training candidates by their cosine similarity to reference gradients computed from real validation data, ensuring that only the most informative synthetic samples are injected into training. The pipeline is evaluated on a real X-ray dataset of 127 scanned WEEE devices annotated across four battery categories. Under a limited-data regime of 400 real training images, our best configuration, gradient-selected CycleGAN-translated synthetic data at +30 images per class, achieves 0.621 mAP50:95 and 0.864 mAP50, surpassing the limited-data real-only baseline (0.563/0.832) evaluated on the same held-out test set, and reaching a performance level comparable to that of a full-data reference model trained on 800 real images (0.590/0.850, evaluated on a separate test split). Ablation studies confirm that gradient-based selection consistently outperforms random sampling under matched budgets, and that domain translation provides additional complementary gains. Overall, the main outcome of this research is that adding only 120 curated synthetic images yields an absolute gain of +0.058 mAP50:95, a 10.3% relative improvement over the real-only baseline under identical training and evaluation conditions. These results demonstrate that carefully curated synthetic augmentation can compensate for real data scarcity in industrial X-ray inspection, with direct implications for scalable automated WEEE recycling.