Given the limited computing capabilities of UAV terminal equipment, there is a challenge in balancing the accuracy and computational cost when deploying the target detection model for forest fire detection on the UAV. Additionally, the fire targets photographed by the UAV are small and prone to misdetection and omission during detection. This paper proposes a lightweight, small target detection model, FL-YOLOv7, based on YOLOv7. First, we designed a light module, C3GhostV2, to replace the feature extraction module in YOLOv7. Simultaneously, we used the Ghost module to replace some of the standard convolution layers in the backbone network, accelerating inference speed and reducing model parameters. Secondly, we introduced the Parameter-Free Attention (SimAm) attention mechanism to highlight the features of smoke and fire targets and suppress background interference, improving the model’s representation and generalization performance without increasing network parameters. Finally, we incorporated the Adaptive Spatial Feature Fusion (ASFF) module to address the model’s weak small target detection capability and use the loss function with dynamically adjustable sample weights (WIoU) to weaken the impact of low-quality or complex samples and improve the model’s overall performance. Experimental results show that FL-YOLOv7 reduces the parameter count by 27% compared to the YOLOv7 model while improving 2.9% ${mAP50}_{small}$ and 24.4 frames per second in FPS, demonstrating the effectiveness and superiority of our model in small target detection, as well as its real-time and reliability in forest fire scenarios.