To accurately identify the safety hazards at hydropower construction sites in real time

this paper combines the channel attention mechanism and spatial attention mechanism

improves and applies the YOLOv8 algorithm

and develops an automatic identification method of safety hazards in hydropower construction based on the dual attention mechanism. First

based on the YOLOv8 network framework

we construct a channel attention mechanism to highlight key features adaptively

strengthen dynamically the expression of image features of hidden danger areas

and suppress the influence of background noise. Then

a spatial attention mechanism is built that helps weight important regions

reduce background interference

and optimize feature fusion. It allows to adjust attention adaptively

enhance local detail capture and the positioning accuracy

improve the multi-scale target detection ability

and enhance the spatial feature representation ability of the model. Finally

we verify the accuracy and reliability of the model through a case study of an ongoing construction project. The results show that the proposed method identifies the hazards effectively against the interference in the construction site through the attention mechanism

and achieves an accuracy rate of up to 86.2%

better than previous identification models

thereby improving the dynamic management

prevention and control of hydropower construction safety hazards.