Anchor-free deep learning based object detection is a mainstream single-stage object detection algorithm. An hourglass network structure that incorporates multiple layers of supervisory information can significantly improve the accuracy of the anchor-free object detection algorithm, but its speed is much lower than that of a common network at the same level, and the features of different scale objects will interfere with each other. In order to solve the above problems, an object detection algorithm based on asymmetric hourglass network structure was proposed. The proposed algorithm is not constrained by the shape and size when fusing the features of different network layers, and can quickly and efficiently abstract the semantic information of network, making it easier for the model to learn the differences between various scales. Aiming at the problem of object detection at different scales, a multi-scale output hourglass network structure was designed to solve the problem of feature mutual interference between different scale objects and refine the output detection results. In addition, a special non-maximum suppression algorithm for multi-scale outputs was used to improve the recall rate of the detection algorithm. Experimental results show that the AP50 index of the proposed algorithm on Common Objects in COntext (COCO) dataset reaches 61.3%, which is 4.2 percentage points higher than that of anchor-free network CenterNet. The proposed algorithm surpasses the original algorithm in the balance of accuracy and time, and is particularly suitable for real-time object detection in industry.