Han Liu

Background: The forest environment is intricate and dynamic, with its depiction influenced by various factors such as geographical location, weather conditions, and capture angles. Relying solely on flame or smoke is insufficient for precise fire information. Aims: This paper proposes a method for accurate detection on forest flame and smoke based on improved feature extraction module with enhanced image processing. Methods: We a fusion-guided filtering image processing method and flame segmentation strategy to augment the quality of dataset. Additionally, an outstanding extraction backbone, incorporating ghost modules and decoupled fully connected (DFC) attention modules, is developed to increase the model’s receptive field. Furthermore, the ELAN-S neck with SimAM attention mechanism is introduced to fuse features from the backbone network, facilitating the extraction of shallow and deep-level semantic information. Key results: Compared to YOLOV7, our model demonstrates superior performance with a 5% increase in mean average precision (mAP), a 4.3% increase in average precision for small objects (APS), and a 3–4% enhancement in other metrics. Conclusions: The proposed model achieves a good balance between detection speed and detection accuracy. The improved model performs well in real forest fire detection scenarios. Implications: In the early forest fire detection, the model considers both flame and smoke information to describe the fire situation, and effectively combines the semantic information of both for fire warning.

Forest resources are one of the indispensable resources of the earth, which are the basis for the survival and development of human society. With the swift advancements in computer vision and artificial intelligence technology, the utilization of deep learning for smoke detection has achieved remarkable results. However, the existing deep learning models have poor performance in forest scenes and are difficult to deploy because of numerous parameters. Hence, we introduce an optimized forest fire smoke monitoring system for embedded edge devices based on a lightweight deep learning model. The model makes full use of the multi-scale variable attention mechanism of Transformer architecture to strengthen the ability of image feature extraction. Considering the needs of application scenarios, we propose an improved lightweight network model LCNet for feature extraction, which can reduce the parameters and enhance detecting ability. In order to improve running speed, a simple semi-supervised label knowledge distillation scheme is used to enhance the overall detection capability. Finally, we design and implement a forest fire smoke detection system on an embedded device, including the Jetson NX hardware platform, high-definition camera, and detection software system. The lightweight model is transplanted to the embedded edge device to achieve rapid forest fire smoke detection. Also, an asynchronous processing framework is designed to make the system highly available and robust. The improved model reduces three-fourths of the parameters and increases speed by 3.4 times with similar accuracy to the original model. This demonstrates that our system meets the precision demand and detects smoke in time.

UAVs are commonly used in forest fire detection, but the captured fire images often suffer from blurring due to the rapid motion between the airborne camera and the fire target. In this study, a multi-input, multi-output U-Net architecture that combines spatial domain and frequency domain information is proposed for image deblurring. The architecture includes a multi-branch dilated convolution attention residual module in the encoder to enhance receptive fields and address local features and texture detail limitations. A feature-fusion module integrating spatial frequency domains is also included in the skip connection structure to reduce feature loss and enhance deblurring performance. Additionally, a multi-channel convolution attention residual module in the decoders improves the reconstruction of local and contextual information. A weighted loss function is utilized to enhance network stability and generalization. Experimental results demonstrate that the proposed model outperforms popular models in terms of subjective perception and quantitative evaluation, achieving a PSNR of 32.26 dB, SSIM of 0.955, LGF of 10.93, and SMD of 34.31 on the self-built forest fire datasets and reaching 86% of the optimal PSNR and 87% of the optimal SSIM. In experiments without reference images, the model performs well in terms of LGF and SMD. The results obtained by this model are superior to the currently popular SRN and MPRNet models.

Various studies have shown that it is necessary to estimate the drivers’ vigilance to reduce the occurrence of traffic accidents. Most existing EEG-based vigilance estimation studies have been performed on intra-subject and multi-channel signals, and these methods are too costly and complicated to implement in practice. Hence, aiming at the problem of cross-subject vigilance estimation of single-channel EEG signals, an estimation algorithm based on capsule network (CapsNet) is proposed. Firstly, we propose a new construction method of the input feature maps to fit the characteristics of CapsNet to improve the algorithm accuracy. Meanwhile, the self-attention mechanism is incorporated in the algorithm to focus on the key information in feature maps. Secondly, we propose substituting the traditional multi-channel signals with the single-channel signals to improve the utility of algorithm. Thirdly, since the single-channel signals carry fewer dimensions of the information compared to the multi-channel signals, we use the conditional generative adversarial network to improve the accuracy of single-channel signals by increasing the amount of data. The proposed algorithm is verified on the SEED-VIG, and Root-mean-square-error (RMSE) and Pearson Correlation Coefficient (PCC) are used as the evaluation metrics. The results show that the proposed algorithm improves the computing speed while the RMSE is reduced by 3%, and the PCC is improved by 12% compared to the mainstream algorithm. Experiment results prove the feasibility of using forehead single-channel EEG signals for cross-subject vigilance estimation and offering the possibility of lightweight EEG vigilance estimation devices for practical applications.

Forest fires have continually endangered personal safety and social property. To reduce the occurrences of forest fires, it is essential to detect forest fire smoke accurately and quickly. Traditional forest fire smoke detection based on convolutional neural networks (CNNs) needs many hand-designed components and shows poor ability to detect small and inconspicuous smoke in complex forest scenes. Therefore, we propose an improved early forest fire smoke detection model based on deformable transformer for end-to-end object detection (deformable DETR). We use deformable DETR as a baseline containing the best sparse spatial sampling for smoke with deformable convolution and relation modeling capability of the transformer. We integrate a Multi-scale Context Contrasted Local Feature module (MCCL) and a Dense Pyramid Pooling module (DPPM) into the feature extraction module for perceiving features of small or inconspicuous smoke. To improve detection accuracy and reduce false and missed detections, we propose an iterative bounding box combination method to generate precise bounding boxes which can cover the entire smoke object. In addition, we evaluate the proposed approach using a quantitative and qualitative self-made forest fire smoke dataset, which includes forest fire smoke images of different scales. Extensive experiments show that our improved model’s forest fire smoke detection accuracy is significantly higher than that of the mainstream models. Compared with deformable DETR, our model shows better performance with improvement of mAP (mean average precision) by 4.2%, AP_S (AP for small objects) by 5.1%, and other metrics by 2% to 3%. Our model is adequate for early forest fire smoke detection with high detection accuracy of different-scale smoke objects.