关键词: font-family:-apple-system, blinkmacsystemfont, ", font-size:14px, background-color:#FFFFFF, ">计算机视觉, 石化企业, 火灾智能监测, 网络结构, 激活函数, 损失函数, 评价指标, 消融实验

Abstract: A fire identification algorithm based on the YOLOv5 algorithm is proposed to address the complex interference factors at fire scenes and the relative lag of traditional fire alarm methods. Firstly, the activation function is improved to enhance the model's nonlinear expression and generalization ability, and the loss function is improved to reduce the learning of unnecessary features by the model. Then, the attention mechanism (CBAM) is introduced to enhance the model's bidirectional perception in both channels and space. Finally, a comparative experiment between the improved YOLOv5 algorithm and other recognition algorithms is designed. Qualitative and quantitative analysis proves the effectiveness of the improved algorithm. Compared with the original YOLOv5 algorithm, the model's average precision (AP) has been improved by 5.85%, and the overall network performance has been significantly improved, meeting the requirements for accuracy and real-time detection of fire images. The test results prove its good performance and application value.

Key words: font-family:-apple-system, blinkmacsystemfont, ", font-size:14px, background-color:#FFFFFF, ">computer vision, petrochemical enterprise, fire intelligent monitoring, network structure, activation function, loss function, evaluation index, ablation experiment