基于改进MobileNetV3的选煤厂实时火灾识别方法研究

Study of improved MobileNetV3-based coal preparation plant fire recognition method

选煤厂的早期火灾识别可以极大保障人员和财产的安全,然而现有的火灾识别方法往往存在误报率高、实时性差等问题,难以满足实际需求。为了解决选煤厂实时火灾识别效果不理想的问题,基于计算机视觉领域中网络结构、轻量级网络和注意力机制的研究现状,针对目前选煤厂等工业生产环境中火灾识别方法存在识别延迟、推理速度较慢、难以进行实时识别等问题,提出将注意力模块CBSAM(Convolutional Block Second-order Attention Module)集成到Mobile-NetV3网络中来增强模型的表示能力,解决选煤厂的实时火灾识别问题。为提升特征表示能力,CBSAM模块在平均池化和最大池化特征的基础上引入二阶池化特征,通过通道注意力模块生成通道注意力图,然后通过空间注意力模块生成空间注意力图。为验证该模型在选煤厂实时火灾识别中的有效性,数据集采用开源数据集Fire Dunning Dataset和选煤厂现场收集的数据,通过消融实验验证CBSAM模块的有效性,通过对比实验验证CBSAM模块与改进MobileNetV3网络的泛化性与有效性。结果表明:通过引入CBSAM模块与MobileNetV3网络相结合,实现了轻量级网络与高效特征提取的有机结合,提高了网络的分类效果;CBSAM-MobileNetV3收集到的选煤厂火灾识别数据集具有良好的识别准确率。在MobileNetV3网络的基础上通过集成CBSAM模块减少了冗余信息的干扰,网络分类效果明显优于其他轻量级网络架构,能够有效地提升火灾识别的准确率,可以应用于选煤厂火灾识别任务。

Real time recognition of early fire breaking out in coal preparation plant can greatly ensure the safety of personnel and properties.However,the recognition methods currently applied suffer the drawbacks of being high in misrecognition rate and slow in real-time response,making it difficult,therefore,to meet the actual needs.To deal with the unsatisfactory situation,an improved MobileNetV3-based real-time recognition model is proposed.The attention Module CBSAM(Convolutional Block Second-order Attention Module)is integrated into the MobileNetV3 network to enhance the module′s representation ability,so as to tackle the problems currently encountered,such as delay in recognition in industrial environment,slow inference speed,and difficulty in making real-time fire recognition. The paper outlines the current state of the research work on network structure, lightweight network, and attention mechanism in the field of computer vision. On the basis of the CBSAM mod-ule′s average and maximum pooling features, the 2nd-order pooling feature is introduced in the module to im-prove its feature representation ability, which makes it possible to generate the channel attention images by channel attention module, and spatial attention images by the spatial attention module. For verifying the effect-iveness of the model for real-time recognition of fire in coal preparation plant, the effectiveness of the CBSAM module is verified through ablation experiment using the Fire Dunning Database plus the data actually collected in plant. Through comparative experiments, the generalization and effectiveness of CBSAM module and Mobi-leNetV3 network are verified. Results show that integration of CBSAM module with MobileNetV3 network can realize organic fusion of lightweight network and high-efficiency feature extraction, and improve the network′s classification effect;the CBSAM-MobileNetV3 has a high recognition accuracy on the plant-collected dataset;and the integration of CBSAM and MobileNetV3 can lead to reduced interference of redundant information, much better classification effect than those of other lightweight network architectures, and noticeably higher ac-curacy and applicability in recognition of fire in coal preparation.