Yolov5 和 yolov8 算法在检测人类个人防护装备方面的效果比较

S. A. Filichkin, S. V. Vologdin
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引用次数: 0

摘要

文章对 YOLO 类卷积神经网络的不同架构进行了比较分析:YOLOv5 和 YOLOv8。神经网络是在由 2,200 多张有标记的数字图像组成的数据集上进行训练的。对医用口罩、手套、头盔、护目镜、制服等 8 类个人防护设备进行了检测和识别。为了评估物体检测算法的效率,使用了神经网络的准确度指标、学习时间和内存大小等特征。研究证实,YOLO 算法在检测和识别数字静态和视频图像中的物体方面具有很高的效率和准确性。研究表明,YOLOv8 的 mAP 物体检测平均准确率比 YOLOv5 高 3%,而新算法的性能比早期版本的神经网络提高了 50%以上。所获得的结果可用于改进各领域的物体检测系统,如汽车工业、医疗和科学研究、安防领域等。根据实验结果,对人类个人防护设备检测算法的选择做出了结论。该领域的进一步研究可能基于扩大训练数据集的数量,以提高物体识别的准确性,并评估算法在大量数据上的性能。计划在优化卷积神经网络结构方面开展进一步研究,以提高数字图像中物体检测的效率、速度和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of the Effectiveness of Yolov5 and yolov8 Algorithms for Detecting Human Personal Protective Equipment
The article deals with the comparative analysis of different architectures of convolutional neural networks of YOLO class: YOLOv5 and YOLOv8. Neural network was trained on a dataset consisting of more than 2,200 marked digital images. Detection and recognition of 8 classes of personal protective equipment such as medical masks, gloves, helmets, goggles, uniforms and others was performed. To evaluate the efficiency of the object detection algorithms such characteristics as accuracy metrics, learning time and memory size of the neural networks were used. The research confirms the high efficiency and accuracy of the YOLO algorithms in the detection and recognition of objects in digital still and video images. The research shows that the average accuracy of mAP object detection in YOLOv8 is 3% higher than in YOLOv5, while the performance in the new algorithm has increased by more than 50% compared with the earlier version of the neural network. The results obtained can be used to improve the object detection system in various fields, such as the automotive industry, medical and scientific research, the security field, and etc. Based on the results of the experiment, conclusions were made regarding the selection of an algorithm for the detection of human personal protective equipment. Further research in this area may be based on expanding the volume of training datasets to improve the accuracy of object recognition and evaluate the performance of algorithms on large amounts of data. Further research is planned in the area of optimizing the architecture of convolutional neural networks to improve the efficiency, speed and accuracy of object detection in digital images.
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