Detecting safety helmet wearing in surveillance videos is an essential task for safety management, compliance with regulations, and reducing the death rate from construction industry accidents. However, it is much challenged by some factors like interocclusion, scale variances, perspective distortion, small object detection, and the carrier recognition of safety helmet. Traditional image‐based methods suffer from them. This article proposes a new methodology for detecting safety helmet wearing, which makes use of convolutional neural network‐based face detection and bounding‐box regression for safety helmet detection. On the one hand, the method can help to recognize the carrier of the safety helmet and detect a multiscale and small safety helmet. On the other hand, deep transfer learning based on DenseNet is introduced and applied using two different strategies, namely, object feature extractor and fine‐tuning for safety helmet recognition. To further improve the recognition accuracy, the network model with two peer DenseNet networks is trained by mutual distillation. Extensive analysis and experiments show that the novel methodology has considerable advantages in detecting safety helmet wearing compared to other state‐of‐the‐art models. The proposed model has achieved 96.2% recall, 96.2% precision, and 94.47% average detection accuracy. These results, precision‐recall (PR) curve, and receiver operating characteristic (ROC) curve demonstrate the feasibility of the new model.

中文翻译：

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通过边界框回归和深度转移学习检测建筑工地上的安全帽

检测监视视频中戴的安全帽是安全管理，合规性并降低建筑行业事故死亡率的重要任务。但是，它受到一些因素的挑战，例如相互闭塞，比例尺变化，透视变形，小物体检测以及安全帽的携带者识别。传统的基于图像的方法受其困扰。本文提出了一种用于检测安全头盔磨损的新方法，该方法利用基于卷积神经网络的面部检测和边界框回归技术来检测安全头盔。一方面，该方法可以帮助识别安全头盔的携带者并检测多尺度和小型安全头盔。另一方面，引入和应用了基于DenseNet的深度迁移学习，并使用了两种不同的策略，即对象特征提取和用于安全帽识别的微调。为了进一步提高识别精度，通过相互提炼来训练具有两个对等DenseNet网络的网络模型。大量的分析和实验表明，与其他最新模型相比，该新方法在检测安全帽佩戴方面具有显着优势。提出的模型实现了96.2％的召回率，96.2％的精度和94.47％的平均检测精度。这些结果，精确召回（PR）曲线和接收机工作特性（ROC）曲线证明了新模型的可行性。为了进一步提高识别精度，通过相互提炼来训练具有两个对等DenseNet网络的网络模型。大量的分析和实验表明，与其他最新模型相比，该新方法在检测安全帽佩戴方面具有显着优势。提出的模型实现了96.2％的召回率，96.2％的精度和94.47％的平均检测精度。这些结果，精确召回（PR）曲线和接收机工作特性（ROC）曲线证明了新模型的可行性。为了进一步提高识别精度，通过相互提炼来训练具有两个对等DenseNet网络的网络模型。大量的分析和实验表明，与其他最新模型相比，该新方法在检测安全帽佩戴方面具有显着优势。提出的模型实现了96.2％的召回率，96.2％的精度和94.47％的平均检测精度。这些结果，精确召回（PR）曲线和接收机工作特性（ROC）曲线证明了新模型的可行性。精度为2％，平均检测精度为94.47％。这些结果，精确召回（PR）曲线和接收机工作特性（ROC）曲线证明了新模型的可行性。精度为2％，平均检测精度为94.47％。这些结果，精确召回（PR）曲线和接收机工作特性（ROC）曲线证明了新模型的可行性。