In order to solve the problem of low accuracy of current target detection algorithms in the process of tiny person recognition and location, and improve the detection ability of tiny person, this paper proposes a tiny person detection algorithm AF-RetinaNet based on adaptive fusion and feature refinement. Firstly, the algorithm combines the feature enhancement module with ResNet to build a feature extraction network and uses a parallel structure to obtain enhanced features. Secondly, the context adaptive learning module is used to obtain the feature information of the target context, so as to pay attention to the differences of similar features and alleviate the problem of false detection. Finally, the feature refinement module with the idea of image super-resolution is constructed to enlarge and reconstruct the target feature information, optimize the feature expression ability of small targets and alleviate the problem of missed detection. On the TinyPerson dataset, the average precision of the AF-RetinaNet algorithm reaches 56.78%, and the missed rate reaches 85.38%. Compared with the research benchmark based on the RetinaNet algorithm, the average precision is improved by 5.57%, and the missed rate is reduced by 3.67%. The experimental results show that the model can effectively improve the accuracy of tiny person detection and recognition.