To improve the performance of the forest fire smoke detection model and achieve a better balance between detection accuracy and speed, an improved YOLOv4 detection model (MoAm-YOLOv4) that combines a lightweight netwo...To improve the performance of the forest fire smoke detection model and achieve a better balance between detection accuracy and speed, an improved YOLOv4 detection model (MoAm-YOLOv4) that combines a lightweight network and attention mechanism was proposed. Based on the YOLOv4 algorithm, the backbone network CSPDarknet53 was replaced with a lightweight network MobilenetV1 to reduce the model’s size. An attention mechanism was added to the three channels before the output to increase its ability to extract forest fire smoke effectively. The algorithm used the K-means clustering algorithm to cluster the smoke dataset, and obtained candidate frames that were close to the smoke images;the dataset was expanded to 2000 images by the random flip expansion method to avoid overfitting in training. The experimental results show that the improved YOLOv4 algorithm has excellent detection effect. Its mAP can reach 93.45%, precision can get 93.28%, and the model size is only 45.58 MB. Compared with YOLOv4 algorithm, MoAm-YOLOv4 improves the accuracy by 1.3% and reduces the model size by 80% while sacrificing only 0.27% mAP, showing reasonable practicability.展开更多
Existing almost deep learning methods rely on a large amount of annotated data, so they are inappropriate for forest fire smoke detection with limited data. In this paper, a novel hybrid attention-based few-shot learn...Existing almost deep learning methods rely on a large amount of annotated data, so they are inappropriate for forest fire smoke detection with limited data. In this paper, a novel hybrid attention-based few-shot learning method, named Attention-Based Prototypical Network, is proposed for forest fire smoke detection. Specifically, feature extraction network, which consists of convolutional block attention module, could extract high-level and discriminative features and further decrease the false alarm rate resulting from suspected smoke areas. Moreover, we design a metalearning module to alleviate the overfitting issue caused by limited smoke images, and the meta-learning network enables achieving effective detection via comparing the distance between the class prototype of support images and the features of query images. A series of experiments on forest fire smoke datasets and miniImageNet dataset testify that the proposed method is superior to state-of-the-art few-shot learning approaches.展开更多
为保证对船舶动力舱极早期火灾探测的及时性、准确性和可靠性,需要对烟雾浓度检测原理进行改进和烟雾浓度算法的创新。基于电容式检测元胞结构对烟雾浓度检测原理进行全新设计,并采用多尺度烟雾粒子浓度检测算法对检测信号进行处理,以...为保证对船舶动力舱极早期火灾探测的及时性、准确性和可靠性,需要对烟雾浓度检测原理进行改进和烟雾浓度算法的创新。基于电容式检测元胞结构对烟雾浓度检测原理进行全新设计,并采用多尺度烟雾粒子浓度检测算法对检测信号进行处理,以计算出烟雾粒子浓度。试验结果表明:新设计的探测器可实现对0~10%obs/m浓度烟雾粒子的有效检测,检测精度高于百万分比浓度(Parts Per Million,PPM)级;探测器的灵敏度可以达到PPM级;在存在一定浓度的油气和不同粒径灰尘的使用环境中,仍能实现高于PPM级精度的烟雾粒子浓度检测。展开更多
文摘To improve the performance of the forest fire smoke detection model and achieve a better balance between detection accuracy and speed, an improved YOLOv4 detection model (MoAm-YOLOv4) that combines a lightweight network and attention mechanism was proposed. Based on the YOLOv4 algorithm, the backbone network CSPDarknet53 was replaced with a lightweight network MobilenetV1 to reduce the model’s size. An attention mechanism was added to the three channels before the output to increase its ability to extract forest fire smoke effectively. The algorithm used the K-means clustering algorithm to cluster the smoke dataset, and obtained candidate frames that were close to the smoke images;the dataset was expanded to 2000 images by the random flip expansion method to avoid overfitting in training. The experimental results show that the improved YOLOv4 algorithm has excellent detection effect. Its mAP can reach 93.45%, precision can get 93.28%, and the model size is only 45.58 MB. Compared with YOLOv4 algorithm, MoAm-YOLOv4 improves the accuracy by 1.3% and reduces the model size by 80% while sacrificing only 0.27% mAP, showing reasonable practicability.
基金The work was supported by the National Key R&D Program of China(Grant No.2020YFC1511601)Fundamental Research Funds for the Central Universities(Grant No.2019SHFWLC01).
文摘Existing almost deep learning methods rely on a large amount of annotated data, so they are inappropriate for forest fire smoke detection with limited data. In this paper, a novel hybrid attention-based few-shot learning method, named Attention-Based Prototypical Network, is proposed for forest fire smoke detection. Specifically, feature extraction network, which consists of convolutional block attention module, could extract high-level and discriminative features and further decrease the false alarm rate resulting from suspected smoke areas. Moreover, we design a metalearning module to alleviate the overfitting issue caused by limited smoke images, and the meta-learning network enables achieving effective detection via comparing the distance between the class prototype of support images and the features of query images. A series of experiments on forest fire smoke datasets and miniImageNet dataset testify that the proposed method is superior to state-of-the-art few-shot learning approaches.
文摘为保证对船舶动力舱极早期火灾探测的及时性、准确性和可靠性,需要对烟雾浓度检测原理进行改进和烟雾浓度算法的创新。基于电容式检测元胞结构对烟雾浓度检测原理进行全新设计,并采用多尺度烟雾粒子浓度检测算法对检测信号进行处理,以计算出烟雾粒子浓度。试验结果表明:新设计的探测器可实现对0~10%obs/m浓度烟雾粒子的有效检测,检测精度高于百万分比浓度(Parts Per Million,PPM)级;探测器的灵敏度可以达到PPM级;在存在一定浓度的油气和不同粒径灰尘的使用环境中,仍能实现高于PPM级精度的烟雾粒子浓度检测。
