利用具有 Efficient-Net-B0 架构的卷积神经网络,通过计算机图像识别技术识别有机和非有机废物

Heny Indriani Sutomo
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摘要

本研究旨在利用基于 Efficient-Net-B0 架构的卷积神经网络(CNN)的计算机图像识别技术,开发一种识别有机和非有机废物的方法。高效、准确的废物识别对于可持续废物管理非常重要。本研究的主要目标是区分图像中的有机和非有机废物。手动将废物图像标记为有机或非有机是一项耗时且容易出错的任务。配置和微调 EfficientNet-B0 架构和 CNN 参数以获得最佳性能可能是一个复杂而反复的过程。可能需要进行超参数调整。确保准确的标签对于训练可靠的模型至关重要。选择使用带有 EfficientNet-B0 架构的卷积神经网络 (CNN) 是解决方案的关键部分。EfficientNet-B0 以其在准确性和计算效率之间的平衡而著称。在这项任务中使用 CNN 和 EfficientNet-B0 表明该系统有能力辨别两种废物类型之间的视觉差异。本研究提出的方法利用了 CNN 研究垃圾图像重要特征的能力来识别各种类型的垃圾。本研究包括废物数据收集阶段,其中包括二维图像形式的有机和无机废物。为了评估所提出方法的性能,使用从预定环境中提取的垃圾数据集进行了测试。测试结果表明,所提出的方法能够高度准确地识别有机和非有机废物。在测试场景中,该方法的准确率达到了 98%,证明了其有效识别垃圾类型的能力。通过使用基于 CNN 的计算机图像识别技术和 Efficient-Net-B0 架构,这项研究成功地解决了自动准确识别有机和非有机废物的问题。所提出的方法有望应用于更高效的废物管理系统,有助于最大限度地减少人为识别错误,并为环境保护工作做出积极贡献。这项研究有望成为以可持续方式引进和管理废物的进一步发展基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Organic and Non-Organic Waste with Computer Image Recognition using Convolutionalneural Network with Efficient-Net-B0 Architecture
This study aims to develop a method for identifying organic and non-organic waste using a computer image recognition technique based on Convolutional Neural Network (CNN) with Efficient-Net-B0 architecture. Efficient and accurate waste identification is important in sustainable waste management. The primary goal of this research is to distinguish between organic and non-organic waste in images. Manually labeling waste images as organic or non-organic can be a time-consuming and error-prone task. Configuring and fine-tuning the EfficientNet-B0 architecture and CNN parameters for optimal performance can be a complex and iterative process. Hyperparameter tuning may be needed. Ensuring accurate labels is essential for training a reliable model. The choice of using the Convolutional Neural Network (CNN) with the EfficientNet-B0 architecture is a crucial part of the solution. EfficientNet-B0 is known for its balance between accuracy and computational efficiency. The use of CNNs and EfficientNet-B0 for this task indicates the system's ability to discern visual differences between the two waste types. The method proposed in this study utilizes CNN's ability to study important features of waste images to recognize various types of waste. This research includes the waste data collection stage which includes organic and non-organic waste in the form of 2D images. To evaluate the performance of the proposed method, a test was carried out using a waste dataset taken from a predetermined environment. The test results show that the proposed method is able to identify organic and non-organic waste with a high degree of accuracy. In test scenarios, this method achieves an accuracy of 98%, which demonstrates its ability to effectively identify the type of waste. Through the use of CNN-based computer image recognition techniques with the Efficient-Net-B0 architecture, this research succeeded in solving the problem of identifying organic and non-organic waste automatically and accurately. The proposed method has the potential to be applied in more efficient waste management systems, helps minimize human identification errors, and makes a positive contribution to environmental protection efforts. This research is expected to be the basis for further development in the introduction and management of waste in a sustainable manner.
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