Intelligent defect detection of insulators is faster, more accurate, standardized, and cheaper than manual detection with necessary massive inspection work. Insulator datasets are important for training detection models. Nevertheless, public datasets are scarce and lack variety, which hampers improving detection accuracy and achieving industrial-grade accuracy. We construct a comprehensive beyond the current insulator dataset—the diverse insulator dataset (DINS). DINS contains over 10000 insulator images involving three insulator types (porcelain, glass, and composite) and defects. We annotate over 25000 bounding boxes for object detection and 9000 masks, for instance, segmentation. DINS has much more scale and diversity than the current insulator datasets. Eventually, we discussed the effective augmentation methods for DINS and conducted some experiments demonstrating the usefulness of DINS with 98.3% mean average precision (mAP) of object detection and 97.2% mAP of instance segmentation. The datasets are available on GitHub.

中文翻译：

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DINS：用于对象检测和实例分割的多样化绝缘体数据集


绝缘子的智能缺陷检测比人工检测所需的大量检查工作更快、更准确、更标准化、更便宜。绝缘体数据集对于训练检测模型非常重要。然而，公共数据集稀缺且缺乏多样性，这阻碍了提高检测精度和实现工业级精度。我们构建了一个超越当前绝缘子数据集的综合性绝缘子数据集（DINS）。 DINS 包含超过 10000 个绝缘体图像，涉及三种绝缘体类型（陶瓷、玻璃和复合材料）和缺陷。我们注释了超过 25000 个用于对象检测的边界框和 9000 个掩模（例如分割）。 DINS 比当前的绝缘子数据集具有更大的规模和多样性。最后，我们讨论了 DINS 的有效增强方法，并进行了一些实验，证明了 DINS 的有用性，目标检测平均精度 (mAP) 为 98.3%，实例分割 mAP 为 97.2%。这些数据集可在 GitHub 上获取。