In the task of pedestrian target detection, challenges such as occlusion, changes in scale, and complex backgrounds can significantly degrade detection performance. Based on YOLOv7-tiny network, an improved pedestrian target detection algorithm DR-YOLO (deformable region YOLO) was proposed. First, the ELAN (efficient layer aggregation network) module was integrated with the lightweight deformable convolution DCNv4 (deformable convolution network v4) to establish interaction between long-range and short-range feature information, thereby enhancing the model's ability to express pedestrian features. Second, a multi-scale feature fusion structure was used in the neck to fuse the features of different scale targets, enhancing the model's adaptability to scale changes and reducing the number of parameters. In addition, an improved attention mechanism was added to the main body of the network to enhance the model's focus on interesting targets, thereby optimizing the subsequent network's feature extraction. This improved the model's detection performance in complex situations. Experimental outcomes indicate that the methodology introduced herein attains mAP_0.5 scores of 80% and 46.8% for mAP_0.5-0.95, when evaluated on a dataset comprising CrowdHuman and WiderPerson. Relative to the baseline model, these findings denote enhancements of 1.8% and 2.7% respectively, concurrently with a diminution of 17.2% in parameter count. Compared with similar algorithms at the current stage, without increasing the number of parameters, the detection accuracy of the model has been improved, demonstrating certain superiority.