Infrared target detection is an important means of remote search and monitoring, and the accuracy of infrared tiny target detection determines the practical application value of this method. A detection framework based on a multi-hop deep network is proposed to improve the performance of tiny target detection in complex backgrounds. First, to deal with the"weak"and"small"shape characteristics of tiny targets, an anchor-free mechanism is used to build feature pyramids as the backbone for extracting feature maps. Then, to realize progressive feature interaction and adaptive feature fusion, a multi-hop fusion block composed of multi-scale dilation convolution groups is designed at the connection level. Finally, to reduce the sensitivity to position perturbations of tiny targets, the Wasserstein distance between the real and predicted targets is used as a similarity measure. The experimental results show that compared to existing methods, the proposed method delivers better detection performance in terms of accuracy and efficiency.