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A lightweight deep neural network with higher accuracy
To improve accuracy of the MobileNet network, a new lightweight deep neural network is designed based on the MobileNetV2 network. Firstly, it modifies the network depth of MobileNetV2 to balance the image resolution, network width and depth to keep the gradient stable, which reduces the generation o...
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Published in: | PloS one 2022-08, Vol.17 (8), p.e0271225-e0271225 |
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description | To improve accuracy of the MobileNet network, a new lightweight deep neural network is designed based on the MobileNetV2 network. Firstly, it modifies the network depth of MobileNetV2 to balance the image resolution, network width and depth to keep the gradient stable, which reduces the generation of gradient vanishing or gradient exploding. Secondly, it proposes an improved Bottleneck module by introducing channel attention mechanism. It assigns different weights for different channels according to the degree of relevance between the object features and channels. Therefore, the network can extract more effective features from a complex background. In the end, a new usage strategy of the improved Bottleneck is proposed. It uses the improved Bottleneck module in the second, fourth and fifth stages of MobileNetV2, and uses the original Bottleneck module in other states. Compared with MobileNetV2, MobileNetV3, ShuffleNetV2, GhostNet and HBONetmethods, the proposed method has the highest classification accuracy on the ImageNet-1K dataset, CIFAR-10 and CIFAR-100. Compared with YOLOV4-Lite methods based on these lightweight network networks, YOLOV4-Lite based on our proposed network also has the highest detection accuracy on the PASCAL VOC07+12 dataset. |
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Firstly, it modifies the network depth of MobileNetV2 to balance the image resolution, network width and depth to keep the gradient stable, which reduces the generation of gradient vanishing or gradient exploding. Secondly, it proposes an improved Bottleneck module by introducing channel attention mechanism. It assigns different weights for different channels according to the degree of relevance between the object features and channels. Therefore, the network can extract more effective features from a complex background. In the end, a new usage strategy of the improved Bottleneck is proposed. It uses the improved Bottleneck module in the second, fourth and fifth stages of MobileNetV2, and uses the original Bottleneck module in other states. Compared with MobileNetV2, MobileNetV3, ShuffleNetV2, GhostNet and HBONetmethods, the proposed method has the highest classification accuracy on the ImageNet-1K dataset, CIFAR-10 and CIFAR-100. Compared with YOLOV4-Lite methods based on these lightweight network networks, YOLOV4-Lite based on our proposed network also has the highest detection accuracy on the PASCAL VOC07+12 dataset.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0271225</identifier><identifier>PMID: 35917311</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Accuracy ; Analysis ; Artificial neural networks ; Biology and Life Sciences ; Channels ; Classification ; Computer and Information Sciences ; Datasets ; Engineering and Technology ; Fault diagnosis ; Feature extraction ; Image processing ; Image resolution ; Lightweight ; Modules ; Neural networks ; Power ; Research and Analysis Methods ; Social Sciences</subject><ispartof>PloS one, 2022-08, Vol.17 (8), p.e0271225-e0271225</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Zhao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Zhao et al 2022 Zhao et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c669t-ad8223e67b29c39a09d2ac67f41118fec676198a5645a95ccf3867b0907a2ec93</citedby><cites>FETCH-LOGICAL-c669t-ad8223e67b29c39a09d2ac67f41118fec676198a5645a95ccf3867b0907a2ec93</cites><orcidid>0000-0002-9499-1911</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2697325010/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2697325010?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids></links><search><contributor>Saha, Sriparna</contributor><creatorcontrib>Zhao, Liquan</creatorcontrib><creatorcontrib>Wang, Leilei</creatorcontrib><creatorcontrib>Jia, Yanfei</creatorcontrib><creatorcontrib>Cui, Ying</creatorcontrib><title>A lightweight deep neural network with higher accuracy</title><title>PloS one</title><description>To improve accuracy of the MobileNet network, a new lightweight deep neural network is designed based on the MobileNetV2 network. Firstly, it modifies the network depth of MobileNetV2 to balance the image resolution, network width and depth to keep the gradient stable, which reduces the generation of gradient vanishing or gradient exploding. Secondly, it proposes an improved Bottleneck module by introducing channel attention mechanism. It assigns different weights for different channels according to the degree of relevance between the object features and channels. Therefore, the network can extract more effective features from a complex background. In the end, a new usage strategy of the improved Bottleneck is proposed. It uses the improved Bottleneck module in the second, fourth and fifth stages of MobileNetV2, and uses the original Bottleneck module in other states. Compared with MobileNetV2, MobileNetV3, ShuffleNetV2, GhostNet and HBONetmethods, the proposed method has the highest classification accuracy on the ImageNet-1K dataset, CIFAR-10 and CIFAR-100. Compared with YOLOV4-Lite methods based on these lightweight network networks, YOLOV4-Lite based on our proposed network also has the highest detection accuracy on the PASCAL VOC07+12 dataset.</description><subject>Accuracy</subject><subject>Analysis</subject><subject>Artificial neural networks</subject><subject>Biology and Life Sciences</subject><subject>Channels</subject><subject>Classification</subject><subject>Computer and Information Sciences</subject><subject>Datasets</subject><subject>Engineering and Technology</subject><subject>Fault diagnosis</subject><subject>Feature extraction</subject><subject>Image processing</subject><subject>Image resolution</subject><subject>Lightweight</subject><subject>Modules</subject><subject>Neural networks</subject><subject>Power</subject><subject>Research and Analysis Methods</subject><subject>Social 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subjects | Accuracy Analysis Artificial neural networks Biology and Life Sciences Channels Classification Computer and Information Sciences Datasets Engineering and Technology Fault diagnosis Feature extraction Image processing Image resolution Lightweight Modules Neural networks Power Research and Analysis Methods Social Sciences |
title | A lightweight deep neural network with higher accuracy |
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