Unsafe behaviors of individuals are the most contributing cause of accidents in the laboratories of universities, with improper use of personal protective equipment being the most prominent manifestation. Existing methods are primarily used in construction and industrial settings, focusing solely on whether personnel are wearing protective equipment, without effectively identifying the completeness and effectiveness of the personal protective equipment wearing. This study divides the wearing state of PPE into fine labels and proposes a two-stage personal unsafe behavior detection methods based on object detection and attribute recognition algorithms. In the first stage, the improved YOLOv5-DETR-LPE algorithm is used to achieve the precise detecting of personnel under complex background and low-quality image conditions in the laboratory. In the second stage, the attribute recognition algorithm based on EfficientNet-B3 is used to recognize the unsafe behaviors of detected personnel. The results shows that YOLOv5-DETR-LPE achieves a 1.15% improvement in accuracy and a 5.11% increase in mAP50:95 in the self-built dataset compared to YOLOv5n, with only a slight increase in model parameters and computational load. The EfficientNet-B3 algorithm maintains high accuracy in the recognition of all 11 labels of three attributes. Finally, the recognition and early warning system is designed and implemented in an actual environment, verifying the effectiveness and feasibility of the system in practical scenarios.