Flame recognition algorithm based on improved SqueezeNet
Addressing the efficiency limitations of existing flame recognition algorithms,a lightweight and efficient deep learn-ing model was designed.The model was optimized based on SqueezeNet,then the dual-branch attention mechanism was intro-duced to enhance the recognition capability of flame features,and the classification performance of the model was improved.Meanwhile,the residual connections were incorporated to increase the training stability and feature representation capacity of network.The batch channel normalization technology was employed to enhance the generalization performance of the model.Furthermore,the 3×3 standard convolution kernels in the Fire module were replaced with depth-wise separable convolutions,further reducing the parameter amount and computational complexity.The performance of the algorithm was evaluated using multiple public flame image datasets.The results show that compared to the original SqueezeNet algorithm,the improved SqueezeNet model not only enhances the detection speed,but also achieves higher recognition accuracy and better generaliza-tion ability.This research results can provide theoretical foundation and technical support for the development of real-time fire monitoring systems and intelligent firefighting equipment.
NETL
NSTL
万方数据
