发表机构
Institute of Innovation, Science and Sustainability, Federation University Australia（澳大利亚联邦大学创新、科学与可持续发展研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对数据准备中图像去噪的不足，提出过滤劣质图像的方法，用图像质量评估指标和阈值筛选，确保有足够图像用于深度学习模型开发，实验表明该方法在交通和对象识别数据上性能优于现有方法，有实际应用潜力。
AI 中文摘要
过滤噪声是数据准备的基本部分，可提高对象分割、检测和识别等应用的图像质量。文献中提出了各种降噪技术，卷积神经网络（CNN）因其能从数据中提取复杂模式和特征而在图像去噪中受到欢迎。基于CNN的技术存在需要合适训练数据集和调整所有图像大小的缺点，且所有这些滤波技术都只适用于某些类型的环境和相机噪声。为弥补这一差距，本文首次提出一种过滤劣质图像的方法，用图像质量评估指标评估质量，并用最佳阈值过滤劣质图像，同时确保有足够数量的图像用于开发深度学习模型。使用真实和模拟交通及对象识别数据的结果表明，该方法优于现有方法。交通标志识别数据集的平均识别准确率为93.8%，对象识别数据集为84.9%，表明该模型在自动驾驶等实际应用中的潜力。
英文摘要
Filtering noise is a fundamental part of data preparation that enhances image quality for applications such as object segmentation, detection, and recognition. Various noise reduction techniques are proposed in the literature, including the use of median, Gaussian, and bilateral filters. Convolutional neural networks (CNNs) have gained popularity in image denoising owing to their ability to extract complex patterns and features from data. CNNs are highly adaptable, making them effective tools for various image-denoising tasks. One drawback of CNN-based techniques is that they require an appropriate training dataset and all images to be resized. Another notable drawback of all these filtering techniques is that they work for certain types of environmental and camera noises. To bridge this research gap, in this paper, for the first time, instead of denoising, we propose an approach that filters out poor-quality images for various environmental and camera impacts. In our approach, quality is assessed using an image quality assessment metric and an optimum threshold is used to filter out poor-quality images. We also ensure that a sufficient number of images remain to develop the deep learning (DL) model. The results produced using real and simulated traffic and object recognition data demonstrate the performance supremacy of the proposed approach compared with the state-of-the-art approaches. The average recognition accuracy for our proposed approach is 93.8% for the traffic sign recognition dataset and 84.9% for the object recognition dataset. This indicates our model's potential for real-life applications such as autonomous vehicles.
Comments11 pages
