As a very challenging project in target detection, small target detection is widely distributed in daily life. In video surveillance scenarios, pedestrians’ faces about 20 meters away from the camera can be considered small targets. Due to the possibility of mutual occlusion of faces and their susceptibility to noise and weather, lighting conditions, the performance of existing target detection models on such small targets is inferior to that on medium and large targets. To address these issues, this study proposes an improved YOLOv7 model with a high-resolution detection head and transforms the backbone network based on GhostNetV2. At the same time, the PANet structure is replaced by the BiFPN and SA attention modules combined to enhance the multi-scale feature fusion capability; the original CIoU loss function is improved by combining the Wasserstein distance, reducing the sensitivity of small targets to anchor frame position offset. This study conducts comparative experiments on the public dataset VisDrone2019 and a self-made video surveillance dataset. Results show that the mAP of the improved method proposed in this study improved to 50.1% on the VisDrone2019 dataset and is 1.6 percentage points higher than existing methods on the self-made video surveillance dataset, which effectively improves the ability of small target detection and achieves good real-time performance on the GTX1080Ti.