In the context of large-scale fire areas and complex forest environments, the task of identifying the subtle features and aspects of fire can pose a significant challenge for the deep learning model. As a result, to enhance the model’s ability to represent features and its precision in detection, this study initially introduces ConvNeXtV2 and Conv2Former to the You Only Look Once version 7 (YOLOv7) algorithm, separately, and then compares the results with the original YOLOv7 algorithm through experiments. After comprehensive comparison, the proposed ConvNeXtV2-YOLOv7 based on ConvNeXtV2 exhibits a superior performance in detecting forest fires. Additionally, in order to further focus the network on the crucial information in the task of detecting forest fires and minimize irrelevant background interference, the efficient layer aggregation network (ELAN) structure in the backbone network is enhanced by adding four attention mechanisms: the normalization-based attention module (NAM), simple attention mechanism (SimAM), global attention mechanism (GAM), and convolutional block attention module (CBAM). The experimental results, which demonstrate the suitability of ELAN combined with the CBAM module for forest fire detection, lead to the proposal of a new method for forest fire detection called CNTCB-YOLOv7. The CNTCB-YOLOv7 algorithm outperforms the YOLOv7 algorithm, with an increase in accuracy of 2.39%, recall rate of 0.73%, and average precision (AP) of 1.14%.

To tackle the problem of missed detections in long-range detection scenarios caused by the small size of forest fire targets, initiatives have been undertaken to enhance the feature extraction and detection precision of models designed for forest fire imagery. In this study, two algorithms, DenseM-YOLOv5 and SimAM-YOLOv5, were proposed by modifying the backbone network of You Only Look Once version 5 (YOLOv5). From the perspective of lightweight models, compared to YOLOv5, SimAM-YOLOv5 reduced the parameter size by 28.57%. Additionally, although SimAM-YOLOv5 showed a slight decrease in recall rate, it achieved improvements in precision and average precision (AP) to varying degrees. The DenseM-YOLOv5 algorithm achieved a 2.24% increase in precision, as well as improvements of 1.2% in recall rate and 1.52% in AP compared to the YOLOv5 algorithm. Despite having a higher parameter size, the DenseM-YOLOv5 algorithm outperformed the SimAM-YOLOv5 algorithm in terms of precision and AP for forest fire detection.

Due to its unique geographical and climatic conditions, the Liangshan Prefecture region is highly prone to large fires. There is an urgent need to study the growth rate of fire-burned areas to fill the research gap in this region. To address this issue, this study uses the Grey Wolf Optimizer (GWO) algorithm to optimize the hyperparameters in the eXtreme Gradient Boosting (XGBoost) model, constructing a GWO-XGBoost model. Finally, the optimized ensemble model (GWO-XGBoost) is used to create a fire growth rate warning map for the Liangshan Prefecture in Sichuan Province, China, filling the research gap in forest fire studies in this area. This study comprehensively selects factors such as monthly climate, monthly vegetation, terrain, and socio–economic aspects and incorporates monthly reanalysis data from forest fire assessment systems in Canada, the United States, and Australia as features to construct the forest fire dataset. After collinearity tests to filter redundant features and Pearson correlation analysis to explore features related to the burned area growth rate, the Synthetic Minority Oversampling Technique (SMOTE) is used to oversample the positive class samples. The GWO algorithm is used to optimize the hyperparameters in the XGBoost model, constructing the GWO-XGBoost model, which is then compared with XGBoost, Random Forest (RF), and Logistic Regression (LR) models. Model evaluation results showed that the GWO-XGBoost model, with an AUC value of 0.8927, is the best-performing model. Using the SHapley Additive exPlanations (SHAP) value analysis method to quantify the contribution of each influencing factor indicates that the Ignition Component (IC) value from the United States National Fire Danger Rating System contributes the most, followed by the average monthly temperature and the population density. The growth rate warning map results indicate that the southern part of the study area is the key fire prevention area.

Forest fires not only cause severe damage to ecosystems and biodiversity but also directly threaten the safety of human societies. Given the significant increase in both the frequency and intensity of forest fires worldwide, especially under extreme climate conditions, efficient fire detection and initial attack (IA) are particularly critical. The initial attack is a key stage in forest fire control, and the time taken for fire detection is a crucial factor influencing the success of the initial attack. In response to the challenges of forest fire prevention and control, this study explores Unmanned Aerial Vehicle (UAV) cruising strategies, aiming to develop appropriate approaches based on regional characteristics and provide efficient periodic monitoring solutions for areas with high ecological value and challenging accessibility. By optimizing UAV patrol routes, this research seeks to maximize coverage in areas with lower initial attack success rates and significantly reduce fire detection time, thereby improving detection efficiency. We developed and applied four optimization strategies, random search, high-risk first (HRF), nearest high-risk first (NHRF), and a genetic algorithm-based (GA-based) strategy, to compare different UAV flight routes. To evaluate the deployment effectiveness of the four UAV cruise strategies, we introduced two evaluation metrics: Average Grid Risk (AGR) and Average Distance Risk (ADR). Experimental results showed that the NHRF and GA-based strategies performed better. Specifically, NHRF achieved the highest high-risk coverage, ranging from 51.5% to 71.3%, significantly outperforming the random search strategy (4–7%) and the HRF strategy (23.1–37.5%). The GA-based algorithm achieved the highest grid coverage, ranging from 30% to 59.8%, far surpassing the random search strategy (4–6.6%) and the HRF strategy (10.2–19.1%). Additionally, the NHRF and GA-based strategies delivered the best AGR and ADR performance, respectively. The application of these innovative strategies and evaluation metrics enhances forest fire prevention through periodic monitoring and supports more efficient firefighting efforts.

When forest fires occur, timely detection and initial attack are critical for fire prevention. This study focuses on optimizing the cruise path of Unmanned Aerial Vehicles (UAVs) from the perspective of initial attack. It aims to maximize coverage of regions where initial attack success rates are low, shorten the time taken to detect fires, and, in turn, boost detection effectiveness and the initial attack success. In this paper, a path planning strategy, Improved Multi-Objective Crested Porcupine Optimizer (IMOCPO), is proposed. This strategy employs a weighted sum approach to formulate a composite objective function that balances global search and local optimization capabilities, considering practical requirements such as UAV endurance and uneven distribution of risk areas, thus enhancing adaptability in complex forest environments. The weight selection is justified through systematic grid search and validated by sensitivity analysis. The proposed strategy was compared and evaluated with a related strategy using four metrics: high-risk coverage rate, grid coverage rate, Average Distance Risk (ADR), and Average Grid Risk (AGR). Results show that the proposed path planning strategy performs better in these metrics. This study provides an effective solution for optimizing UAV cruise strategies in forest fire monitoring and has practical significance for improving the intelligence of forest fire prevention.