FER(Face expression recognition) plays an important role in human-computer interaction, but it is also one of the hot problems that artificial intelligence is difficult to solve. In recent years, as researchers have intensively studied the field of FER, the number of network models applied to FER has also increased. Relying on their strong feature extraction ability, CNN(Convolutional neural networks) have gradually become the main model in the field of FER. However, the excessive amount of parameters of CNN limits its application scenarios, and the lightweight technique for CNN brings about the degradation of face expression feature extraction ability. To address the above problems, this paper proposes an improved MobileNeXt-based expression recognition network. Based on the MobileNext lightweight network , the model improves its feature extraction capability. Firstly, the SandGlass block in the network can enhance the transmission of feature information in the network and reduce the loss of expression features during transmission. Secondly, the Ghost module is used to replace the 1×1 convolution kernel in the network to reduce the number of parameters in the feature extraction layer. Then use Drop-Activation layer instead of ReLU layer in Sandglass block to enhance the generalization ability and accuracy of the network. Finally, the Spatial Group-wise Enhance attention mechanism is introduced to enhance the network's ability to refine the expression features. The experimental results show that the network model improves the expression recognition accuracy by 2.6%, 6.5% and 7.15% in FER2013, RAF-DB and CK+ datasets, respectively, while the parameter and floating-point operations only increase 0.85M and 2.93M compared with MobileNet V2.
The occurrence of fire threatens people's lives and property damage. An effective way to reduce flame damage is to detect flames in video images and take appropriate measures to contain fires at an early stage of occurrence. To solve the problem of low detection precision of current image-based flame detection methods, we propose a high precision flame detector based on yolov5 improvement. Coordinate Attention Blocks are introduced in the backbone network to obtain both channel relationships and long-range dependencies with precise positional information and increase the feature expression of the backbone network. Swin Transformer Blocks are introduced in the neck to expand the receptive field of the network and improve the flame feature extraction; the Adaptive Spatial Feature Fusion module is introduced in the head network to enhance the multi-scale feature fusion of flame features and reduce false alarms. Compared with the yolov5l, the precision of the model is improved by 4.1%. Compared with other existing detectors, it achieves the best average precision of 66.8%.
The occurrence of fire threatens people's lives and property damage. An effective way to reduce flame damage is to detect flames in video images and take appropriate measures to contain fires at an early stage of occurrence. To solve the problem of low detection precision of current image-based flame detection methods, we propose a high precision flame detector based on yolov5 improvement. Coordinate Attention Blocks are introduced in the backbone network to obtain both channel relationships and long-range dependencies with precise positional information and increase the feature expression of the backbone network. Swin Transformer Blocks are introduced in the neck to expand the receptive field of the network and improve the flame feature extraction; the Adaptive Spatial Feature Fusion module is introduced in the head network to enhance the multi-scale feature fusion of flame features and reduce false alarms. Compared with the yolov5l, the precision of the model is improved by 4.1%. Compared with other existing detectors, it achieves the best average precision of 66.8%
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