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基于WGAN-GP的建筑垃圾数据集的优化与扩充
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Abstract:
随着人工智能技术的火爆与不断成熟，人们越来越倾向于用神经网络的方式去解决现有的问题。同时随着城市化的推进，建筑的拆毁与重建过程中产生了大量的建筑垃圾。现有的建筑垃圾回收装置回收工序复杂，效率低下，导致回收成本远远高于资源本身价值，因此探索高效率，低成本的建筑垃圾回收设备尤为重要。然而现在几乎不存在针对建筑垃圾的专有数据集，且大多数数据质量都不高，导致神经网络无法应用于建筑垃圾领域。为了解决上述问题，提高神经网络在建筑垃圾方面的应用，本文提出了一个新的建筑垃圾数据集，通过手工采集与清理的方式形成初始数据集，在通过WGAN-GP模型对其完成高质量的样本扩充，从而填补建筑垃圾数据集的空白。
With the explosion and continuous maturity of artificial intelligence technology, people are increasingly inclined to use neural networks to solve existing problems. Meanwhile, with the advancement of urbanization, the process of building demolition and reconstruction has produced a large amount of construction waste. The existing construction waste recycling devices have complicated recycling processes and low efficiency, resulting in recycling costs much higher than the value of the resources themselves, so it is important to explore high-efficiency and low-cost construction waste recycling devices. However, there are almost no proprietary data sets for construction waste, and most of the data are of low quality, which makes it impossible to apply neural networks to the construction waste field. In order to solve the above problems and improve the application of neural networks in construction waste, this paper proposes a new construction waste dataset, the initial dataset is formed by manual collection and cleaning, and then it is expanded with high quality samples by WGAN-GP model to fill the gap of construction waste dataset.
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