A lossless lightweight CNN design for SAR target recognition

Zhang, Fan; Liu, Yingbing; Zhou, Yongsheng; Yin, Qiang; Li, Heng-Chao

doi:10.1080/2150704x.2020.1730472

Cited by 34 publications

(23 citation statements)

References 13 publications

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“…CNN [16] automatically extracts image features; thus, the complex feature extraction and data reconstruction process of conventional recognition algorithms can be avoided. AlexNet [17], VGGNet [18][19][20][21], GoogLeNet [22,23], ResNet [24][25][26][27], and other networks can be adopted for vehicle type recognition, whereas for the limitations of sample quality and quantity as well as the defects of network feature extraction and classification performance, vehicle recognition exhibits relatively low accuracy.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Type Recognition Algorithm Based on Improved Network in Network

Zhu

2021

Complexity

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Vehicle type recognition algorithms are broadly used in intelligent transportation, but the accuracy of the algorithms cannot meet the requirements of production application. For the high efficiency of the multilayer perceptive layer of Network in Network (NIN), the nonlinear features of local receptive field images can be extracted. Global average pooling (GAP) can avoid the network from overfitting, and small convolution kernel can decrease the dimensionality of the feature map, as well as downregulate the number of model training parameters. On that basis, the residual error is adopted to build a novel NIN model by altering the size and layout of the original convolution kernel of NIN. The feasibility of the algorithm is verified based on the Stanford Cars dataset. By properly setting weights and learning rates, the accuracy of the NIN model for vehicle type recognition reaches 97.2%.

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Section: Related Workmentioning

confidence: 99%

Vehicle Type Recognition Algorithm Based on Improved Network in Network

Zhu

2021

Complexity

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“…Chen et al [28] combined a weight-based pruning with an adaptive architecture squeezing to obtain a high compression ratio in CNN, and utilized pruning to find an appropriate squeezing ratio. In [29], a lossless lightweight CNN design strategy is explored for the SAR target recognition by using the structured pruning and the knowledge distillation. Pruning methods can reduce the amount of parameters while reducing the computational complexity, but it is possible that the accuracy is degraded due to the retraining of each network layer iteratively.…”

Section: Related Workmentioning

confidence: 99%

Human Segmentation Based on Compressed Deep Convolutional Neural Network

et al. 2020

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Most semantic segmentation models based on deep convolutional neural network (CNN) typically require a large number of weight parameters, high hardware resources for storage and computation. Moreover, redesigning a compact network suffers from some training problems, such as under-fitting. A human segmentation algorithm is proposed based on compressed deep CNN to optimize the convolution layers and filters. PSPNet-50 is fine-tuned on the human segmentation dataset to obtain the human segmentation model with higher accuracy. Then the convolutional-layer level pruning and corresponding structure optimization are performed so that the parameters of the model are substantially reduced. Finally, the two-stage global filter-level pruning strategy is used. Compared with the method of layer by layer pruning and retraining, our strategy not only reduces parameters of the model and saves the time of retraining, but also keeps the high IoU (Intersection over Union) accuracy. In addition, by adding auxiliary losses in the network during training CNN, the supervised training of the network is improved, and IoU is further increased. Compared to the model before compression, the sufficient experiments show that the parameter number, computation cost, memory consumption, and parameter storage are decreased by 1/7.5, 5.6/6.6, 0.7/1, 6.5/7.5, respectively, while the segmentation speed is accelerated by 2.4 times, and IoU on test set reaches 93.2%. INDEX TERMS compressed deep convolutional neural network; human segmentation; convolutional-layer level pruning; filter level pruning.

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“…Parameter pruning [26], [36] yields an effective reduction of network size; however, it relies on the support of hardware and a computing library because of sparse parameter kernel after pruning. Convolutional filter pruning [26], [36], [52], [53] resolves the unfriendliness of hardware and Basic Linear Algebra Subprograms (BLAS library). However, the problem of drop accuracy is commonly encountered in both approaches.…”

Section: B Network Compression For Image Recognitionmentioning

confidence: 99%

Efficient and Small Network Using Multi-Trim Network Structure for Tactile Object Recognition on Embedded Systems

et al. 2020

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Tactile object recognition (TOR) is critical in robot perception. However, as an embedded system, a robot brain has a fixed resource budget and is unsuitable for modern convolutional neural networks (CNNs). To bridge this gap, we present a simple network-compression approach that improves the accuracylatency trade-off of the network. The multi-trim network structure (MTNS) is a robust combination of network compression (NC) techniques providing a lightweight network with no performance drop. Furthermore, as an optical tactile sensor, we present a random-dot sensor that obtains rich information with a single touch, thus avoiding modality fusion. The random-dot sensor captures the object shapes and inputs them to TOR. In an experimental evaluation, we compare the performances of the proposed MTNS approach with those of CNN filter pruning, the network quantization technique, an adaptive mixture of low-rank factorizations, and knowledge distillation. The MTNS better resolved the accuracy-latency trade-off in tactile object recognition than the modern NC methods. By combining the random-dot sensor and MTNS approach, TOR enhances the accuracy and processing time performances.

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A lossless lightweight CNN design for SAR target recognition

Cited by 34 publications

References 13 publications

Vehicle Type Recognition Algorithm Based on Improved Network in Network

Vehicle Type Recognition Algorithm Based on Improved Network in Network

Human Segmentation Based on Compressed Deep Convolutional Neural Network

Efficient and Small Network Using Multi-Trim Network Structure for Tactile Object Recognition on Embedded Systems

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