Traditional object detection algorithms often face challenges such as poor detection performance and low detection efficiency. To address these problems, this study proposes a method for detecting small objects based on an improved YOLOv7 network. This method adds more paths to the efficient layer aggregation module (ELAN) of the original network and effectively integrates the feature information from different paths before introducing the selective kernel network (SKNet). This allows the model to pay more attention to features of different scales in the network and extract more useful information. To enhance the model’s perception of spatial information for small objects, an eSE module is designed and connected to the end of ELAN, thus forming a new efficient layer aggregation network module (EF-ELAN). This module preserves image feature information more completely and improves the generalization ability of the network. Additionally, a cross stage-adaptively spatial feature fusion module (CS-ASFF) is designed to address the issue of inconsistent feature scales in small object detection. This module is improved based on the ASFF network and the Nest connection method. It extracts weights through operations such as convolution and pooling on each image of the feature pyramid, applies the feature information to a specific layer, and utilizes other feature layers to enhance the network’s feature processing capabilities. Experimental results show that the proposed algorithm improves the average precision rate by 1.5% and 2.1% on the DIOR and DOTA datasets, respectively, validating its effectiveness in enhancing the detection performance of small objects.