Lightweight YOLO models for object detection based on low-rank decomposition
As train intelligence continues to advance, numerous studies have emerged to explore lightweight techniques for object detection models on onboard equipment, for the purpose of improving calculation efficiency amidst limited resources. This paper proposed a parameter compression algorithm based on l...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Electric Drive for Locomotives
2024-01-01
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| Series: | 机车电传动 |
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| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2024.01.120 |
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| _version_ | 1849728792289869824 |
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| author | LIN Delü LIU Chang CHEN Qi ZENG Yang HE Kun |
| author_facet | LIN Delü LIU Chang CHEN Qi ZENG Yang HE Kun |
| author_sort | LIN Delü |
| collection | DOAJ |
| description | As train intelligence continues to advance, numerous studies have emerged to explore lightweight techniques for object detection models on onboard equipment, for the purpose of improving calculation efficiency amidst limited resources. This paper proposed a parameter compression algorithm based on low-rank decomposition for the You Only Look Once (YOLO) series of object detection models, aiming to overcome the limited versatility of current lightweight treatment methods for these models. Initially, calculations were conducted to determine a low rank using the preset low-rank ratio coefficient along with the number of input and output channels of convolution units. Subsequently, a new convolution sequence was obtained by performing the Tucker decomposition to the convolutional layer of the target structure. Lastly, a new convolution sequence was fused to replace the original convolutional layer. Experiments were conducted on the proposed parameter compression method based on low-rank decomposition, using public datasets and three models, i.e., YOLOv5-l, YOLOv8-x, and YOLOX-x. While ensuring an average detection accuracy of the models based on low-rank decomposition over 96% compared to the original models, the number of parameters and float-point calculations of these models reduced by about 40%. The experiments resulted in a nearly 50% improvement in image detection speed. Further visual displays illustrated similar receptive fields on the same images between the compressed and original models. The experimental results show that the proposed method is effective in lightweight compression of single-stage object detection models of the YOLO series and can enhance their usability for onboard equipment. In addition, this study serves as a valuable reference for the lightweight treatment of other models in the automatic train operation scenarios of rail transit field. |
| format | Article |
| id | doaj-art-cd7ae7f2e22e4015a7592095a62b7607 |
| institution | DOAJ |
| issn | 1000-128X |
| language | zho |
| publishDate | 2024-01-01 |
| publisher | Editorial Department of Electric Drive for Locomotives |
| record_format | Article |
| series | 机车电传动 |
| spelling | doaj-art-cd7ae7f2e22e4015a7592095a62b76072025-08-20T03:09:25ZzhoEditorial Department of Electric Drive for Locomotives机车电传动1000-128X2024-01-0113814450543555Lightweight YOLO models for object detection based on low-rank decompositionLIN DelüLIU ChangCHEN QiZENG YangHE KunAs train intelligence continues to advance, numerous studies have emerged to explore lightweight techniques for object detection models on onboard equipment, for the purpose of improving calculation efficiency amidst limited resources. This paper proposed a parameter compression algorithm based on low-rank decomposition for the You Only Look Once (YOLO) series of object detection models, aiming to overcome the limited versatility of current lightweight treatment methods for these models. Initially, calculations were conducted to determine a low rank using the preset low-rank ratio coefficient along with the number of input and output channels of convolution units. Subsequently, a new convolution sequence was obtained by performing the Tucker decomposition to the convolutional layer of the target structure. Lastly, a new convolution sequence was fused to replace the original convolutional layer. Experiments were conducted on the proposed parameter compression method based on low-rank decomposition, using public datasets and three models, i.e., YOLOv5-l, YOLOv8-x, and YOLOX-x. While ensuring an average detection accuracy of the models based on low-rank decomposition over 96% compared to the original models, the number of parameters and float-point calculations of these models reduced by about 40%. The experiments resulted in a nearly 50% improvement in image detection speed. Further visual displays illustrated similar receptive fields on the same images between the compressed and original models. The experimental results show that the proposed method is effective in lightweight compression of single-stage object detection models of the YOLO series and can enhance their usability for onboard equipment. In addition, this study serves as a valuable reference for the lightweight treatment of other models in the automatic train operation scenarios of rail transit field.http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2024.01.120deep learninglow-rank decompositionautomatic train operationlightweight treatment of modelobject detection |
| spellingShingle | LIN Delü LIU Chang CHEN Qi ZENG Yang HE Kun Lightweight YOLO models for object detection based on low-rank decomposition 机车电传动 deep learning low-rank decomposition automatic train operation lightweight treatment of model object detection |
| title | Lightweight YOLO models for object detection based on low-rank decomposition |
| title_full | Lightweight YOLO models for object detection based on low-rank decomposition |
| title_fullStr | Lightweight YOLO models for object detection based on low-rank decomposition |
| title_full_unstemmed | Lightweight YOLO models for object detection based on low-rank decomposition |
| title_short | Lightweight YOLO models for object detection based on low-rank decomposition |
| title_sort | lightweight yolo models for object detection based on low rank decomposition |
| topic | deep learning low-rank decomposition automatic train operation lightweight treatment of model object detection |
| url | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2024.01.120 |
| work_keys_str_mv | AT lindelu lightweightyolomodelsforobjectdetectionbasedonlowrankdecomposition AT liuchang lightweightyolomodelsforobjectdetectionbasedonlowrankdecomposition AT chenqi lightweightyolomodelsforobjectdetectionbasedonlowrankdecomposition AT zengyang lightweightyolomodelsforobjectdetectionbasedonlowrankdecomposition AT hekun lightweightyolomodelsforobjectdetectionbasedonlowrankdecomposition |