The invention relates to a lightweight remote sensing target detection method based on SEYOLOv3, which belongs to the technical field of target detection, and comprises the following steps: 1, takinga YOLOv3 algorithm as a basic model framework, and in order to reduce network parameters and improve network reasoning speed, designing a lightweight trunk feature extraction network; 2, in order to improve the scale invariance of the features and reduce the over-fitting risk, a spatial pyramid pooling (SPP) algorithm is provided, and pooling of three scales is carried out to obtain an output feature vector with a fixed length; a spatial attention model SE module is introduced, useless information is further compressed, and useful information is enhanced; and 3, updating parameters through iterative training to obtain a final network model, adopting multi-scale prediction by utilizing the model, and predicting a final result through detection heads of three scales. According to the method,while the reasoning speed of the network is effectively improved, the precision is ensured, the feature expression capability of the network is enhanced, and the scale invariance is improved.