In the field of infrared small target detection for unmanned aerial vehicle (UAV), complex ground backgrounds and the inherently small size of targets often result in missed detections or false alarms in detection models. To address this problem, this study proposes a lightweight and high-precision infrared small target detection algorithm based on the YOLOv7 framework, termed unmanned aerial vehicle-you only look once (UAV-YOLO). First, given that the targets of interest are predominantly small, the 1×1 convolutions in the ELAN, ELAN-W, and CARAFE modules of the YOLOv7 backbone network, as well as those in the neck network, are replaced with GSConv. At the same time, the P5 detection head, which provides limited efficiency in infrared small target detection, is removed, and a newly added P2 detection head is designed specifically for small targets. These modifications not only enhance detection efficiency but also substantially reduce the number of parameters, thus achieving a lightweight model. Second, an improved SPPFCSPC pyramid pooling module is integrated into the backbone network. The inclusion of this module effectively expands the receptive field of the model, thus improving the detection accuracy of infrared small targets. Subsequently, the content-aware reassembly of feature (CARAFE) module is incorporated into YOLOv7. This module enhances the preservation and optimization of feature representations for small targets. Furthermore, a coordinate attention (CA) mechanism is introduced before the detection head, enabling more precise localization of small targets and allowing the detection head to focus on key regions. Finally, the normalized Wasserstein distance (NWD) loss is adopted to replace the CIoU loss. This replacement reduces the sensitivity of the model to positional deviations and further improves detection performance. Experimental results demonstrate that compared with the baseline YOLOv7 model, the proposed UAV-YOLO achieves an mAP of 95.7%, representing a 5.2% improvement, while the number of parameters is reduced to 12.0M, a decrease of 67.7%. These improvements ensure high precision while significantly reducing the number of parameters, thus verifying the effectiveness and practicality of the proposed infrared small target detection model for UAV applications.