Human detection is an important problem which has many applications in collaborative manufacturing, such as employee counting and human fatigue detection. Recently, some methods based on convolutional neural network (CNN) have been proposed to solve this problem, but they often suffer from poor generalization due to the lack of real-world datasets from industrial scenarios. In this paper, we propose a graph pooling based human detection method to improve the generalization of existing CNN models. First, we segment an image and abstract it as a graph where nodes represent the segmented regions and the features of nodes are pixel values of the segmentation. Second, a graph pooling model is used to extract coarse-grained but informative features from the graph. Next, the pooled graph is reconstructed to image and input to a popular CNN model (i.e., YOLOv5) for human detection. We perform experiments on data containing a great number of images from industrial scenarios. Experimental results show that the proposed method outperforms the popular CNN model and has better generalization performance.