Aiming at the detection difficulties of small targets in pictures, such as less information and large scale changes, this paper proposes a feature fusion small target dynamic real-time detection model (HCD-YOLOv5s) based on YOLOv5s. In view of the problems that sampling under the model is easy to cause the loss of small target information and insufficient expression of deep network location information, a detection head for detecting small targets is added from the shallow layer; Aiming at the problem of feature confusion caused by feature fusion, this paper designs a feature fusion method CCAT to reduce the loss of location information and semantic information in the detection layer; In view of the inconsistency between the detection task and the activation function adapted to the different data distribution, the DConv module is designed to separate the regression task and the detection task, so as to realize the dynamic detection of the model. In this paper, the Ablation Experiment of the model is carried out on the visdrone data set, and the three modules promote each other. Select pictures with different input sizes to test the speed and accuracy of the model. On the basis of YOLOv5s, the mAP50 of HCD-YOLOv5s is increased by 10.2%, the detection accuracy and parameter quantity are significantly better than YOLOv5m, and the FPS reaches 90. Finally, the experimental verification is carried out on DOTA-v1.0, and the mAP50 and mAP are increased by 1.8% and 2.0% respectively, which proves that the HCD-YOLOv5s proposed in this paper has better performance in small target detection.