ProDMM 与 Bottle-SKU：面向多 SKU 洗发水瓶缺陷检测的新基准和方法
Prototype-Guided Dynamic Domain Modulation and a Large-Scale Multi-SKU Benchmark for Shampoo Bottle Surface Defect Detection
做工业质检的可以看看：新数据集 Bottle-SKU 有 48 种瓶型 2 万多标注框，ProDMM 思路能应对不同 SKU 外观差异，方法细节都在论文里。
研究人员发布了 Bottle-SKU 数据集，包含 13,119 张图像、20,419 个实例级边界框、7 类缺陷和 48 种瓶身 SKU。针对不同 SKU 外观差异导致的同一缺陷表现不一的问题，提出 ProDMM 方法，为每个 SKU 构建视觉原型并生成低秩 Query/Value 调制，同时保留预训练 Grounding DINO 的检测通路。实验在 Bottle-SKU 和公开的 GC10-DET-SKU 金属表面缺陷数据集上验证了原型引导域调制的有效性。
Prototype-Guided Dynamic Domain Modulation and a Large-Scale Multi-SKU Benchmark for Shampoo Bottle Surface Defect Detection
Industrial shampoo-bottle surface defect detection must operate across many stock keeping units (SKUs), where differences in bottle geometry, material, label layout, reflectance, and imaging conditions can substantially alter the appearance of the same defect. Existing industrial defect datasets rarely provide both instance-level defect annotations and product-variant identities, making it difficult to study SKU-aware detection under realistic manufacturing conditions. We introduce Bottle-SKU, a large-scale multi-SKU benchmark for shampoo bottle surface defect detection. Bottle-SKU contains 13,119 images, 20,419 instance-level bounding boxes, seven defect categories, and 48 bottle SKUs, enabling evaluation of both shared defect recognition and SKU-specific appearance variation. To address the challenge of SKU-specific appearance variation, we propose Prototype-Guided Dynamic Domain Modulation (ProDMM). ProDMM constructs a visual prototype for each SKU from training images and predicts a soft SKU distribution for each input. The resulting prototype-conditioned representation dynamically generates low-rank Query/Value modulation for the visual backbone and residual conditioning for defect-text-to-visual attention, while preserving the pretrained Grounding DINO detection pathway. The model is trained jointly with detection, SKU-domain classification, and modulation-regularization objectives. Experiments on Bottle-SKU and the public GC10-DET-SKU metallic surface defect dataset demonstrate the effectiveness of prototype-guided domain conditioning for industrial surface defect detection.