The timely and precise detection of forest fires is critical for halting the spread of wildfires and minimizing ecological and economic damage. However, the large variation in target size and the complexity of the background in UAV remote sensing images increase the difficulty of real-time forest fire detection. To address this challenge, this study proposes a lightweight YOLO model for UAV remote sensing forest fire detection (LUFFD-YOLO) based on attention mechanism and multi-level feature fusion techniques: (1) GhostNetV2 was employed to enhance the conventional convolution in YOLOv8n for decreasing the number of parameters in the model; (2) a plug-and-play enhanced small-object forest fire detection C2f (ESDC2f) structure was proposed to enhance the detection capability for small forest fires; (3) an innovative hierarchical feature-integrated C2f (HFIC2f) structure was proposed to improve the model’s ability to extract information from complex backgrounds and the capability of feature fusion. The LUFFD-YOLO model surpasses the YOLOv8n, achieving a 5.1% enhancement in mAP and a 13% reduction in parameter count and obtaining desirable generalization on different datasets, indicating a good balance between high accuracy and model efficiency. This work would provide significant technical support for real-time forest fire detection using UAV remote-sensing images.

Due to the unique distance and angles involved in satellite remote sensing, ships appear with a small pixel area in images, leading to insufficient feature representation. This results in suboptimal performance in ship detection, including potential misses and false detections. Moreover, the complexity of backgrounds in remote sensing images of ships and the clustering of vessels also adversely affect the accuracy of ship detection. Therefore, this paper proposes an optimized model named SSMA-YOLO, based on YOLOv8n. First, this paper introduces a newly designed SSC2f structure that incorporates spatial and channel convolution (SCConv) and spatial group-wise enhancement (SGE) attention mechanisms. This design reduces spatial and channel redundancies within the neural network, enhancing detection accuracy while simultaneously reducing the model’s parameter count. Second, the newly designed MC2f structure employs the multidimensional collaborative attention (MCA) mechanism to efficiently model spatial and channel features, enhancing recognition efficiency in complex backgrounds. Additionally, the asymptotic feature pyramid network (AFPN) structure was designed for progressively fusing multi-level features from the backbone layers, overcoming challenges posed by multi-scale variations. Experiments of the ships dataset show that the proposed model achieved a 4.4% increase in mAP compared to the state-of-the-art single-stage target detection YOLOv8n model while also reducing the number of parameters by 23%.