Pre-IdentifyNet: An Improved Neural Network for Image Recognition

Shixiong Chen, Xinyang Du, Qingyue Xu, Xiaoru Chen, Jingle Li


With the rise and development of artificial intelligence, image recognition and classification technology has received more and more attention as an important branch of its research field. Among them, the introduction of deep learning networks and the construction of neural network structures not only avoid a lot of the tedious work of manual extraction, but also improve the accuracy of image recognition. Convolutional neural networks have many advantages that conventional neural networks do not have. Therefore, image classification systems based on convolutional neural networks emerge in endlessly, but there is still much room for improvement in terms of recognition accuracy and recognition speed. Based on this, this paper proposes an improved deep convolutional neural network to improve the accuracy of the network by changing a series of parameters such as the number of channels of the convolution layer, the size of the convolution kernel, the learning rate, the number of iterations, and the size of the small batch with speed. In this paper, three data sets were selected, namely sewage, animals and the Simpson Family. Comparing the improved convolutional neural network network with the existing SqueezeNet and GoogleNet. It is found that the accuracy of the network is maintained while maintaining a similar speed. Both F1-score and F1-score have been improved with a higher recognition rate and better recognition effect in image recognition classification.


Convolutional Neural Network; Image Recognition and Classification; Pre-IdentifyNet

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Liu L, Ma Y, Zhang X, et al. High discriminative SIFT feature and feature pair selection to improve the bag of visual words model. IET Image Processing 2017; 11(11): 994-1001. doi: 10.1049/iet-ipr.2017.0062.

Jiang Y, Chi Z. A CNN model for semantic person part segmentation with capacity optimization. IEEE Transactions on Image Processing 2018; 28(5): 2465-2478. doi: 10.1109/TIP.2018.2886785.

Alex K, Ilya S, Geoffrey H. ImageNet classification with deep convolutional neural networks. NIPS 2012. doi: 10.1145/3065386.

Mopuri KR, Garg U, Babu RV. CNN fixations: An unraveling approach to visualize the discriminative image regions. IEEE Transactions on Image Processing 2017; 1-11. doi: 10.1109/TIP.2018.2881920.

Ge L, Liang H, Yuan J, et al. Real-time 3D hand pose estimation with 3D convolutional neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 2018; 1-14.

Hou Q, Cheng M, Hu X, et al. Deeply supervised salient object detection with short connections. IEEE Transactions on Pattern Analysis and Machine Intelligence 2018; 1-14.

Zeng K, Wang Y, Mao J, et al. A local metric for defocus blur detection based on CNN feature learning. IEEE Transactions on Image Processing 2019; 28(5): 2107-2115. doi: 10.1109/TIP.2018.2881830.

Soon FC, Khaw HY, Chuah JH, et al. PCANet-Based convolutional neural network architecture for a vehicle model recognition system. IEEE Transactions on Intelligent Transportation Systems 2019; 20(2): 749-759. doi: 10.1109/TITS.2018.2833620.



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