基于改进YOLOv5模型的室内占用率自动检测。

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chao Wang, Yunchu Zhang, Yanfei Zhou, Shaohan Sun, Hanyuan Zhang, Yepeng Wang
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引用次数: 9

摘要

室内占用检测对于能效控制和2019冠状病毒病可追溯性至关重要。通过课堂监控视频分析,可以准确地识别和确定人员的数量和位置。这些信息用于管理环境设备,如暖通空调和照明系统,以减少能源使用。然而,主流的单阶段YOLO算法仍然使用基于锚点的机制和对检测头进行预测。这导致模型收敛速度慢,对密集遮挡目标的检测性能差。为此,本文提出了一种新的解耦无锚变焦损失卷积网络算法DFV-YOLOv5来解决这些问题。该方法使用YOLOv5算法作为基准。它使用无锚机制来减少需要启发式调优的设计参数的数量。然后,为了减少模型的耦合,加快模型的收敛能力,提高模型的检测性能,在YOLOv5模型的基础上对检测头进行解耦。它可以解决分类任务和回归任务之间的冲突。此外,我们使用VariFocal loss为困难的数据点分配更多的权重来优化类不平衡问题,并使用训练目标q来度量正样本,对正样本和负样本进行不对称处理。重新设计了总损耗函数,增加了l1损耗,并通过烧蚀实验验证了改进后损耗的效果。通过引入s型线性单元和整流线性单元的混合激活函数,提高了模型的非线性表示,缩短了模型的推理时间。最后,构建了一个教室数据集来验证模型的占用检测性能。在VOC2012、CrowdHuman和自建数据集上,将该模型与主流目标检测模型在平均精度、内存分配、执行时间和参数个数等方面进行了比较。实验结果表明,该方法显著提高了检测精度和鲁棒性,缩短了推理时间,与主流目标检测模型及模型的相关变体相比,证明了该算法在占用检测中的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Automatic detection of indoor occupancy based on improved YOLOv5 model.

Automatic detection of indoor occupancy based on improved YOLOv5 model.

Automatic detection of indoor occupancy based on improved YOLOv5 model.

Automatic detection of indoor occupancy based on improved YOLOv5 model.

Indoor occupancy detection is essential for energy efficiency control and Coronavirus Disease 2019 traceability. The number and location of people can be accurately identified and determined through classroom surveillance video analysis. This information is used to manage environmental equipment such as HVAC and lighting systems to reduce energy use. However, the mainstream one-stage YOLO algorithm still uses an anchor-based mechanism and couples detection heads to predict. This results in slow model convergence and poor detection performance for densely occluded targets. Therefore, this paper proposed a novel decoupled anchor-free VariFocal loss convolutional network algorithm DFV-YOLOv5 for occupancy detection to tackle these problems. The proposed method uses the YOLOv5 algorithm as a baseline. It uses the anchor-free mechanism to reduce the number of design parameters needing heuristic tuning. Afterwards, to reduce the coupling of the model, speed up the model's convergence ability, and improve the model detection performance, the detection head is decoupled based on the YOLOv5 model. It can resolve the conflict between classification and regression tasks. In addition, we use the VariFocal loss to assign more weights to difficult data points to optimize the class imbalance problem and use the training target q to measure positive samples, treating positive and negative samples asymmetrically. The total loss function is redesigned, the L 1 loss is increased, and the ablation experiment verifies the effect of the improved loss. By applying a hybrid activation function of the sigmoid linear unit and rectified linear unit, we improved the model's nonlinear representation and reduced the model's inference time. Finally, a classroom dataset was constructed to validate the occupancy detection performance of the model. The proposed model was compared with mainstream target detection models regarding average mean precision, memory allocation, execution time, and the number of parameters on the VOC2012, CrowdHuman and self-built datasets. The experimental results show that the method significantly improves the detection accuracy and robustness, shortens the inference time, and proves the practicality of the algorithm in occupancy detection compared with the mainstream target detection model and related variants of the model.

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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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