To address the security risks associated with drone "abuse", aiming at the high complexity of the existing deep learning-based drone target detection algorithm, which results in lengthy model trainings, large computing resources, limited input image size, and slow detection speed, a lightweight level drone target detection (DTD-YOLOv4-tiny) algorithm is proposed. The proposed algorithm is based on YOLOv4-tiny, and we optimized the Anchor box using the K-means++ clustering algorithm, added the detection head of the 52×52 size feature map to expand the scope of the algorithm for small targets, and combined it with the ShuffleNetv2 lightweight backbone network, and the reorg_layer downsample and sub-pixel upsample methods were used to optimize the Backbone, Neck, and Head of the YOLOv4-tiny algorithm. Eventually, we obtained the DTD-YOLOv4-tiny with a model size of 1.4 MB and a floating-point calculation (GFLOPs) of 1.1, which is a lightweight detection technique. The experiments demonstrate that the DTD-YOLOv4-tiny detection model does not limit the image input size, while ensuring low computational resource occupation and high real-time detection. Simultaneously, the algorithm with reduced parameters can also maintain accuracy when facing the original large-scale image. When using 960×540 size image as input on the Drone-vs-Bird 2017 dataset, the average precision (AP) @50 of the proposed algorithm achieved 95%, and the detection speed on the RTX2060 graphics card attained 113 frame/s;when using 1920×1080 size image as input on the TIB-Net dataset, the AP@50 of the proposed algorithm achieved 85.1%, and the detection speed on the RTX2080Ti graphics card attained 119 frame/s.