Electric Wires Dataset

### Auto-generated Wires Dataset for Semantic Segmentation with Domain-Independence

In this work, we present a procedure to automatically generate an high-quality training dataset of cable-like objects for semantic segmentation. The proposed method is explained in detail using the recognition of electric wires as a use case. These particular objects are commonly used in an extremely wide set of industrial applications, since they are of information and communication infrastructures, they are used in construction, industrial manufacturing and power distribution. The proposed approach uses an image of the target object placed in front of a monochromatic background. By employing the chroma-key technique, we can easily obtain the training masks of the target object and replace the background to produce a domain-independent dataset. How to reduce the reality gap is also investigated in this work by correctly choosing the backgrounds, augmenting the foreground images exploiting masks. The produced dataset is experimentally validated by training two algorithms and testing them on a real image set. Moreover, they are compared to a baseline algorithm specifically designed to recognise deformable linear objects.

### Cite

@inproceedings{zanella2021auto,
title={Auto-generated Wires Dataset for Semantic Segmentation with Domain-Independence},
author={Zanella, Riccardo and Caporali, Alessio and Tadaka, Kalyan and De Gregorio, Daniele and Palli, Gianluca},
booktitle={2021 International Conference on Computer, Control and Robotics (ICCCR)},
pages={292--298},
year={2021},
organization={IEEE}
}

本研究提出了一种自动生成高质量电缆类对象语义分割训练数据集的方法。该方法以识别电线作为应用案例进行详细阐述。此类对象广泛应用于极其广泛的工业应用中，因其构成信息与通信基础设施，故被用于建筑、工业制造和电力分配等领域。所提出的方法采用将目标对象置于单色背景前方的图像，通过色键技术轻松获取目标对象的训练掩码，并替换背景以生成域无关数据集。此外，本研究还探讨了如何通过恰当地选择背景、利用掩码增强前景图像来降低现实差距。所生成的数据集通过在真实图像集上训练两种算法并对其进行测试进行了实验验证。此外，它们还与专门设计用于识别可变形线性对象的基线算法进行了比较。