利用数据增强的深度学习模型进行物体检测和识别的拟议方法

Ismael M. Abdulkareem, Faris K. AL-Shammri, Noor Aldeen A. Khalid, Natiq A. Omran
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引用次数: 0

摘要

从安全系统到自动驾驶汽车,物体检测和识别在计算机视觉应用中发挥着至关重要的作用。深度学习算法在这些任务中表现出了不俗的性能,但它们通常需要大量的注释数据集来进行训练。然而,收集此类数据集既费时又费钱。数据增强技术通过人为扩展训练数据集来解决这一问题。在本研究中,我们提出了一种利用数据增强技术进行物体检测和识别的深度学习方法。我们使用深度卷积神经网络(CNN)作为底层架构,特别关注流行的模型,如 You Only Look Once version 3(YOLOv3)。通过对训练数据进行各种变换(如旋转、缩放和翻转),我们可以有效增加数据集的多样性和规模。我们的方法不仅提高了模型的鲁棒性和泛化能力,还降低了过度拟合的风险。通过在增强数据上进行训练,模型可以学会从不同视角、尺度和方向识别物体,从而提高准确性和性能。我们在基准数据集上进行了广泛的实验,并使用精度、召回率和平均精度(mAP)等标准指标评估了我们方法的性能。实验结果表明,与没有数据增强的传统训练方法相比,我们基于数据增强的深度学习方法实现了更高的物体检测和识别准确率。我们将 YOLOv3-SPP 模型的平均精度与 YOLOv3 算法的其他两个变体进行了比较:一个是由 53 个卷积层组成的特征提取网络,另一个是由 13 个卷积层组成的特征提取网络。据报告,拟议模型(YOLOv3-SPP)的平均准确率为 97%,F1 分数为 96%,精确度为 94%,平均联合交叉率 (IoU) 为 78.04%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Proposed Approach for Object Detection and Recognition by Deep Learning Models Using Data Augmentation
Object detection and recognition play a crucial role in computer vision applications, ranging from security systems to autonomous vehicles. Deep learning algorithms have shown remarkable performance in these tasks, but they often require large, annotated datasets for training. However, collecting such datasets can be time-consuming and costly. Data augmentation techniques provide a solution to this problem by artificially expanding the training dataset. In this study, we propose a deep learning approach for object detection and recognition that leverages data augmentation techniques. We use deep convolutional neural networks (CNNs) as the underlying architecture, specifically focusing on popular models such as You Only Look Once version 3 (YOLOv3). By augmenting the training data with various transformations, such as rotation, scaling, and flipping, we can effectively increase the diversity and size of the dataset. Our approach not only improves the robustness and generalization of the models but also reduces the risk of overfitting. By training on augmented data, the models can learn to recognize objects from different viewpoints, scales, and orientations, leading to improved accuracy and performance. We conduct extensive experiments on benchmark datasets and evaluate the performance of our approach using standard metrics such as precision, recall, and mean average precision (mAP). The experimental results demonstrate that our data augmentation-based deep learning approach achieves superior object detection and recognition accuracy compared to traditional training methods without data augmentation. We compare the average accuracy of the YOLOv3-SPP model with two other variants of the YOLOv3 algorithm: one with a feature extraction network consisting of 53 convolutional layers and the other with 13 convolutional layers. The average accuracy of the proposed model (YOLOv3-SPP) is reported as accuracy of 97%, F1-score of 96%, precision of 94%, and average Intersection over Union (IoU) of 78.04%.
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