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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. [ABSTRACT FROM AUTHOR]