Object detection algorithms are widely used in the field of production safety. To address the problems of slow detection speed and low detection accuracy in complex construction environments, an improved YOLOX detection algorithm was proposed. First, based on the lightweight convolution module Ghost moudle, the backbone network was reconstructed to compress the model parameters and computational complexity, thereby improving detection speed. Second, embedding the coordinate attention mechanism at the output of the backbone network to enhance the model's ability to learn key position information. Finally, recursive gated convolution was introduced into the neck network to enhance the model's spatial position perception ability and capture long-range dependencies in the image. The improved model are experimentally validated on the Pascal VOC and SHWD datasets, comparing with the baseline model, mean average precision increase by 1.69% and 1.1% respectively, model parameter count decrease by 18.8%, computational load decrease by 23.3%, and frame rate increase by 7.6%. Deploying the purposed model on terminal devices can be applied to real-time monitoring and detection in construction environments.