Fire and smoke detection is crucial for the safe mining of coal energy, but previous fire-smoke detection models did not strike a perfect balance between complexity and accuracy, which makes it difficult to deploy efficient fire-smoke detection in coal mines with limited computational resources. Therefore, we improve the current advanced object detection model YOLOv8s based on two core ideas: (1) we reduce the model computational complexity and ensure real-time detection by applying faster convolutions to the backbone and neck parts; (2) to strengthen the model’s detection accuracy, we integrate attention mechanisms into both the backbone and head components. In addition, we improve the model’s generalization capacity by augmenting the data. Our method has 23.0% and 26.4% fewer parameters and FLOPs (Floating-Point Operations) than YOLOv8s, which means that we have effectively reduced the computational complexity. Our model also achieves a mAP (mean Average Precision) of 91.0%, which is 2.5% higher than the baseline model. These results show that our method can improve the detection accuracy while reducing complexity, making it more suitable for real-time fire-smoke detection in resource-constrained environments.

To conduct an accurate and reliable airport delay prediction will provide an important basis for the macro control of an airspace delay situation and the dynamic allocation of airspace system capacity balance. Accordingly, a method of delay prediction for target airports based on the spatio-temporal delay variables of adjacent airports is proposed in this paper. First, by combining the complex network theory, we first extract the topology of the airport network and create airport clusters with comparable network properties. Second, we develop the TS-BiLSTM-Attention mode to predict the delay per hour for airports in the cluster. As the spatio-temporal feature variables, the arrival delay of airport cluster-associated airports and the delay time series of landing airports are utilized to reach the conclusion. The experimental results indicate that the delay prediction predicated on clusters is superior to that based on data from a single airport. This demonstrates that the delay propagation law derived from cluster data based on spatio-temporal feature extraction can generalize the delay propagation characteristics of airports within clusters.

Unmanned aerial vehicle (UAV) technology is becoming increasingly mature and its cost is significantly reduced, which makes it increasingly popular. However, it also faces some security threats posed by UAVs in low-altitude areas and difficulties in UAV identification. To address this problem, a UAV radio frequency (RF) signal recognition scheme based on a residual bidirectional long short-term memory (Res-BiLSTM) network was proposed. This scheme combines the feature extraction capabilities of residual blocks with the contextual information perception capabilities of the bidirectional long short-term memory Network, and can effectively extract and identify UAV RF signal features. Experimental results show that the method has an experimental accuracy of 99.8% in identifying the presence of a drone, 94.6% in identifying the drone model, and 78.1% in identifying the drone's operating mode. In experiments on drone operation modes, the model achieved a 31.3% improvement in accuracy compared to the baseline model, and outperformed traditional deep learning methods in key indicators such as accuracy, precision, recall, and F1 score.

With the rapid expansion of the civil aviation industry, the surge in flight numbers has led to increasingly pronounced issues of air route congestion and flight conflicts. 4D trajectory prediction, by dynamically adjusting aircraft paths in real time, can prevent air route collisions, alleviate air traffic pressure, and ensure flight safety. Therefore, this paper proposes a combined model—GAT-BiGRU-TPA—based on the Spatio-Temporal Graph Neural Network (STGNN) framework to achieve refined 4D trajectory prediction. This model integrates Graph Attention Networks (GAT) to extract multidimensional spatial features, Bidirectional Gated Recurrent Units (BiGRU) to capture temporal dependencies, and incorporates a Temporal Pattern Attention (TPA) mechanism to emphasize learning critical temporal patterns. This enables the extraction of key information and the deep fusion of spatio-temporal features. Experiments were conducted using real trajectory data, employing a grid search to optimize the observation window size and label length. Results demonstrate that under optimal model parameters (observation window: 30, labels: 4), the proposed model achieves a 45.72% reduction in mean Root Mean Square Error (RMSE) and a 43.40% decrease in Mean Absolute Error (MAE) across longitude, latitude, and altitude compared to the optimal baseline BiLSTM model. Prediction accuracy significantly outperforms multiple mainstream benchmark models. In summary, the proposed GAT-BiGRU-TPA model demonstrates superior accuracy in 4D trajectory prediction, providing an effective approach for refined trajectory management in complex airspace environments.

Aiming at the poor quality of infrared images collected by UAVs in low-visibility environments such as night, and the contradiction between the lightweight deployment of existing detection algorithms on edge devices and detection accuracy, this paper proposes a balance strategy of “structure simplification + attention compensation” based on YOLOv11n as the baseline model. Firstly, the Slim-neck module is reconstructed, and depthwise separable convolution (DWConv) is used to replace the standard convolution. The original dual-branch CSP structure is simplified to a single-branch EfficientVoVGSCSPC module. At the same time, the complex tensor rearrangement operation is removed, which significantly reduces the parameters, GFLOPs, and size of the model. Then, in order to make up for the decline in feature expression ability caused by lightweight design, an ECA module is embedded in the improved GSConv to achieve local enhancement of channel features. In addition, ECA and CBAM are cascaded at the output of EfficientVoVGSCSPC to form a dual-attention module, and the multi-level features are serially refined in the order of “channel-level lightweight calibration → space-level fine weighting”. The experimental results on the self-built UAV infrared pedestrian dataset show that the final model maintained the detection accuracy (mAP@0.5 of 87.5%) while reducing the parameters (42.6%), GFLOPs (28.6%), and model size (39.2%). Compared to the baseline model, although the inference frame rate has decreased by approximately 36%, it offers significant advantages in terms of model parameters, GFLOPs, and size. In addition, the effectiveness of the ECA → CBAM sequence and the advantages of the “single-branch + attention” configuration are verified by comparing different attention cascade sequences and the interaction between residual and attention. This method offers a potential reference for the lightweight design of UAV infrared pedestrian detection, though further validation on actual edge hardware is needed.