Factories play a crucial role in economic and social development. However, fire disasters in factories greatly threaten both human lives and properties. Previous studies about fire detection using deep learning mostly focused on wildfire detection and ignored the fires that happened in factories. In addition, lots of studies focus on fire detection, while smoke, the important derivative of a fire disaster, is not detected by such algorithms. To better help smart factories monitor fire disasters, this paper proposes an improved fire and smoke detection method based on YOLOv8n. To ensure the quality of the algorithm and training process, a self-made dataset including more than 5000 images and their corresponding labels is created. Then, nine advanced algorithms are selected and tested on the dataset. YOLOv8n exhibits the best detection results in terms of accuracy and detection speed. ConNeXtV2 is then inserted into the backbone to enhance inter-channel feature competition. RepBlock and SimConv are selected to replace the original Conv and improve computational ability and memory bandwidth. For the loss function, CIoU is replaced by MPDIoU to ensure an efficient and accurate bounding box. Ablation tests show that our improved algorithm achieves better performance in all four metrics reflecting accuracy: precision, recall, F1, and mAP@50. Compared with the original model, whose four metrics are approximately 90%, the modified algorithm achieves above 95%. mAP@50 in particular reaches 95.6%, exhibiting an improvement of approximately 4.5%. Although complexity improves, the requirements of real-time fire and smoke monitoring are satisfied.

Ship fire may result in significant damage to its structure and large economic loss. Hence, the prompt identification of fires is essential in order to provide prompt reactions and effective mitigation strategies. However, conventional detection systems exhibit limited efficacy and accuracy in detecting targets, which has been mostly attributed to limitations imposed by distance constraints and the motion of ships. Although the development of deep learning algorithms provides a potential solution, the computational complexity of ship fire detection algorithm pose significant challenges. To solve this, this paper proposes a lightweight ship fire detection algorithm based on YOLOv8n. Initially, a dataset, including more than 4000 unduplicated images and their labels, is established before training. In order to ensure the performance of algorithms, both fire inside ship rooms and also fire on board are considered. Then after tests, YOLOv8n is selected as the model with the best performance and fastest speed from among several advanced object detection algorithms. GhostnetV2-C2F is then inserted in the backbone of the algorithm for long-range attention with inexpensive operation. In addition, spatial and channel reconstruction convolution (SCConv) is used to reduce redundant features with significantly lower complexity and computational costs for real-time ship fire detection. For the neck part, omni-dimensional dynamic convolution is used for the multi-dimensional attention mechanism, which also lowers the parameters. After these improvements, a lighter and more accurate YOLOv8n algorithm, called Ship-Fire Net, was proposed. The proposed method exceeds 0.93, both in precision and recall for fire and smoke detection in ships. In addition, the mAP@0.5 reaches about 0.9. Despite the improvement in accuracy, Ship-Fire Net also has fewer parameters and lower FLOPs compared to the original, which accelerates its detection speed. The FPS of Ship-Fire Net also reaches 286, which is helpful for real-time ship fire monitoring.

To review the present scenario of the research on the scheduling and control of the production process in the manufacturing industry, this comprehensive article has extensively examined this field’s hotspots, boundaries, and overall evolutionary trajectory. This paper’s primary goal is to visualize and conduct an organized review of 5052 papers and reviews that were published between 2002 and 2022. To reveal the “social, conceptual, and conceptual framework” of the production area, identify key factors and research areas, highlight major specialties and emerging trends, and conduct research, countries, institutions, literature keywords, etc., are all used. Additionally, research methodologies are always being improved. The aim of this work is to explore more references for research implementation by analyzing and classifying the present research status, research hotspots, and potential future trends in this field of research.

The acceleration of urbanization and the impact of climate change have led to an increasing frequency and intensity of grassland fires, posing severe challenges to resident safety and ecological protection. Traditional static evacuation route planning methods struggle to adapt in real-time to the dynamic changes in fire conditions during emergency management. To address this issue, this paper proposes a grassland fire evacuation route optimization strategy based on the GreyGNN-MARL model. By integrating Synthetic Aperture Radar (Sentinel-1 SAR) imagery, Graph Neural Networks (GNNs), Grey Wolf Optimization (GWO) algorithms, and Multi-Agent Reinforcement Learning (MARL), the model achieves intelligent planning and real-time adjustment of dynamic evacuation routes in fire scenarios. Experimental results demonstrate that this model significantly outperforms traditional methods in terms of evacuation time, risk avoidance success rate, and path safety, with evacuation time reduced by over 25% and risk avoidance success rate improved by approximately 18%. This model provides technical support for emergency management of grassland fires, helping to enhance evacuation efficiency and ensure safety, which is of great significance for smart cities and ecological protection. Future research will focus on further optimizing the model’s computational efficiency and applicability for broader use in fire emergency management in complex environments.