基于轻量化深度学习模型的安全帽检测方法

基于对施工现场管理中安全帽检测重要性的认识,同时考虑工程项目中硬件设施的成本控制等现实问题,本文提出了一种基于深度学习网络Tiny-YOLO v3的轻量化改进版本LT-YOLO (Lighter and Tiny - YOLO)的安全帽检测技术方法。LT-YOLO增加了网络的输出层,并包含一种创新的R-DSC特征提取模块,该模块能够在不改变网络输入与输出大小的前提下,极大地降低模型的复杂度。实验结果表明,LT-YOLO在轻量化效果与检测性能之间取得了优良的平衡,在3.5 M参数量的基础上达到了59.3 mAP(mean average precision)和59.4% Recall。因此LT-YOLO拥有极低的参数量和计算量,对高算力硬件的依赖性低,适用于实际工程管理应用的施工现场安全管理,能够极大地降低企业成本,提升施工安全管理的水平。

Helmet detection method based on lightweight deep learning model

Based on the importance of helmet detection in construction site management and the cost control of hardware facilities in engineering projects, this paper proposes a helmet detection approach based on Lighter and Tiny-YOLO ( LT-YOLO), a lightweight and improved version of the deep learning network Tiny-YOLO v3. The number of prediction layer is increased and an innovative R-DSC feature extraction module is introduced in LT-YOLO. The complexity of the model can be greatly reduced by R-DSC module without changing the size of the network inputs and outputs. The experimental results showed that LT-YOLO achieved an excellent balance between light weight and detection performance, reaching 59.3 mAP (mean average precision) and 59.4% Recall with only 3.5 M parameters. Because of very few parameters and very low computation, LT-YOLO has low dependence on high computing hardware, and is suitable for actual construction site safety management. LT-YOLO can greatly reduce the cost of enterprises and improve the level of construction safety management.