Martian image classification based on iterative pruning VGGNet

VGGNet can provide high-precision Martian image classification，but consumes vast memory resources. Considering the limitation of memory resources of the onboard computer，a Martian image classification method based on iterative pruning VGGNet is proposed to solve this contradiction. Firstly， the transfer learning is used to train the connectivity of the network in order to evaluate the importance of neurons. Secondly，to reduce the number of fully connected layer parameters and memory consumption， the iterative pruning method is used to prune unimportant neurons. Finally，K-means++ clustering is used to quantify the weight parameters，and Huffman coding compresses the weight parameters of VGGNet after iterative pruning and quantization to reduce the storage capacity and floating point arithmetic. Furthermore，the data augmentation is carried out through five data augmentation methods to address the class imbalance. Experimental results show that the memory，Flops and accuracy of the compressed VGGNet model are 62. 63 Mb，150. 6 MFlops and 96. 15%，respectively. Compared with lightweight image classification algorithms such as ShuffleNet，MobileNet and EfficientNet，the performance of the proposed model is better.