塑料垃圾分类的目标检测和缩放模型

Proceedings of the 1st International Conference on AI for People: Towards Sustainable AI, CAIP 2021, 20-24 November 2021, Bologna, Italy Pub Date : 1900-01-01 DOI:10.4108/eai.20-11-2021.2314204

A. Padalkar, Pramod Pathak, Paul Stynes

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引用次数: 3

摘要

．塑料垃圾分类包括将塑料分成不同的塑料类型。本研究提出了一个塑料垃圾分类的目标检测和缩放模型，利用WaDaBa数据集检测四种类型的塑料。本研究比较了scaledyolo4和EfficientDet的目标检测和缩放模型。结果表明，Scaled-Yolov4-CSP比基于颜色直方图的cony - edge - gaussian Filter的准确率高21%。

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An Object Detection and Scaling Model for Plastic Waste Sorting

. Plastic waste sorting involves the separation of plastic into its individual plastic types. This research proposes an Object Detection and Scaling Model for plastic waste sorting to detect four types of plastics using the WaDaBa dataset. This research compares the Object Detection and Scaling Models Scaled-Yolov4 and EfﬁcientDet. Results demonstrate that Scaled-Yolov4-CSP outperforms the state of the art, Colour-Histogram based Canny-Edge-Gaussian Filter, by 21% accuracy.

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Proceedings of the 1st International Conference on AI for People: Towards Sustainable AI, CAIP 2021, 20-24 November 2021, Bologna, Italy

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