Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture
Field manual labor behavior recognition is an important task that applies deep learning algorithms to industrial equipment for capturing and analyzing people’s behavior during field labor. In this study, we propose a field manual labor behavior recognition network based on an enhanced SlowFast archi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/7/2976 |
| _version_ | 1827286951946878976 |
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| author | Mingxin Zou Yanqing Zhou Xinhua Jiang Julin Gao Xiaofang Yu Xuelei Ma |
| author_facet | Mingxin Zou Yanqing Zhou Xinhua Jiang Julin Gao Xiaofang Yu Xuelei Ma |
| author_sort | Mingxin Zou |
| collection | DOAJ |
| description | Field manual labor behavior recognition is an important task that applies deep learning algorithms to industrial equipment for capturing and analyzing people’s behavior during field labor. In this study, we propose a field manual labor behavior recognition network based on an enhanced SlowFast architecture. The main work includes the following aspects: first, we constructed a field manual labor behavior dataset containing 433,500 fast-track frames and 8670 key frames based on the captured video data, and labeled it in detail; this includes 9832 labeled frames. This dataset provides a solid foundation for subsequent studies. Second, we improved the slow branch of the SlowFast network by introducing the combined CA (Channel Attention) attention module. Third, we enhanced the fast branch of the SlowFast network by introducing the ACTION hybrid attention module. The experimental results show that the recognition accuracy of the improved SlowFast network model with the integration of the two attention modules increases by 7.08%. This implies that the improved network model can more accurately locate and identify manual labor behavior in the field, providing a more effective method for problem solving. |
| first_indexed | 2024-04-24T10:48:40Z |
| format | Article |
| id | doaj.art-447cbe7694d04baa9ad6b578b238f9f1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T10:48:40Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-447cbe7694d04baa9ad6b578b238f9f12024-04-12T13:15:21ZengMDPI AGApplied Sciences2076-34172024-04-01147297610.3390/app14072976Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast ArchitectureMingxin Zou0Yanqing Zhou1Xinhua Jiang2Julin Gao3Xiaofang Yu4Xuelei Ma5School of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010011, ChinaSchool of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010011, ChinaSchool of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010011, ChinaSchool of Agriculture, Inner Mongolia Agricultural University, Hohhot 010019, ChinaSchool of Agriculture, Inner Mongolia Agricultural University, Hohhot 010019, ChinaSchool of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010011, ChinaField manual labor behavior recognition is an important task that applies deep learning algorithms to industrial equipment for capturing and analyzing people’s behavior during field labor. In this study, we propose a field manual labor behavior recognition network based on an enhanced SlowFast architecture. The main work includes the following aspects: first, we constructed a field manual labor behavior dataset containing 433,500 fast-track frames and 8670 key frames based on the captured video data, and labeled it in detail; this includes 9832 labeled frames. This dataset provides a solid foundation for subsequent studies. Second, we improved the slow branch of the SlowFast network by introducing the combined CA (Channel Attention) attention module. Third, we enhanced the fast branch of the SlowFast network by introducing the ACTION hybrid attention module. The experimental results show that the recognition accuracy of the improved SlowFast network model with the integration of the two attention modules increases by 7.08%. This implies that the improved network model can more accurately locate and identify manual labor behavior in the field, providing a more effective method for problem solving.https://www.mdpi.com/2076-3417/14/7/2976fieldmanual labor behaviordetection and recognitionSlowFast |
| spellingShingle | Mingxin Zou Yanqing Zhou Xinhua Jiang Julin Gao Xiaofang Yu Xuelei Ma Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture Applied Sciences field manual labor behavior detection and recognition SlowFast |
| title | Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture |
| title_full | Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture |
| title_fullStr | Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture |
| title_full_unstemmed | Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture |
| title_short | Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture |
| title_sort | spatio temporal behavior detection in field manual labor based on improved slowfast architecture |
| topic | field manual labor behavior detection and recognition SlowFast |
| url | https://www.mdpi.com/2076-3417/14/7/2976 |
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