基于增强特征融合网络的安全帽佩戴检测

佩戴安全帽是保证工人施工安全的重要方式之一。现有的安全帽检测器的检测精度与速度都有待提高，这使得这些检测器难以大规模应用于实际的生产活动中。针对这些问题，文中推出了基于EfficientDet的安全帽检测器，并在此基础上从特征融合的角度对其进行了改进。该模型通过使用特征补充的方式减少了特征融合过程中的信息损失，并利用改进的特征金字塔及自适应空间融合模块提升了融合的效率，最终达到提升性能的目的。实验表明，文中改进的模型在安全帽佩戴数据集上的精确率达到83.03%,相较于未改进的模型有所提升，且模型大小没有明显增加。该模型在PASCAL VOC 2007上的精确率则达到了82.76%。

Helmet Wearing Detection Based on Enhanced Feature Fusion Network

Wearing safety helmet is one of the important ways to ensure the safety of workers in production activities. The detection accuracy and speed of the existing helmet detector need to be improved, which makes it difficult for the existing detectors to be applied in real production activities on a large scale. To solve these problems, the helmet detector based on EfficientDet is introduced and improved from the perspective of feature fusion. Specifically, the model reduces the information loss in the process of feature fusion using feature supplement module, and improves the efficiency of feature fusion using improved feature pyramid and adaptive spatial fusion module, and finally achieves the goal of improving performance. Experimental results show that the accuracy of the improved model on the helmet wearing data set is 83.03%, and the model size does not increase significantly. The accuracy of the model on PASCAL VOC 2007 data set is 82.76%.