An optimized lightweight real-time detection network model for IoT embedded devices
Abstract With the rapid development of Internet of Things (IoT) technology, embedded devices in various computer vision scenarios can realize real-time target detection and recognition tasks, such as intelligent manufacturing, automatic driving, smart home, and so on. YOLOv8, as an advanced deep lea...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88439-w |
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| author | Rongjun Chen Peixian Wang Binfan Lin Leijun Wang Xianxian Zeng Xianglei Hu Jun Yuan Jiawen Li Jinchang Ren Huimin Zhao |
| author_facet | Rongjun Chen Peixian Wang Binfan Lin Leijun Wang Xianxian Zeng Xianglei Hu Jun Yuan Jiawen Li Jinchang Ren Huimin Zhao |
| author_sort | Rongjun Chen |
| collection | DOAJ |
| description | Abstract With the rapid development of Internet of Things (IoT) technology, embedded devices in various computer vision scenarios can realize real-time target detection and recognition tasks, such as intelligent manufacturing, automatic driving, smart home, and so on. YOLOv8, as an advanced deep learning model in the field of target detection, has attracted much attention for its excellent detection speed, high precision, and multi-task processing capability. However, since IoT embedded devices typically own limited computing resources, direct deployment of YOLOv8 is a big challenge, especially for real-time detection tasks. To address this vital issue, this work proposes and deploys an optimized lightweight real-time detection network model that well-suits for IoT embedded devices, denoted as FRYOLO. To evaluate its performance, a case study based on real-time fresh and defective fruit detection in the production line is performed. Characterized by low training cost and high detection performance, this model accurately detects various types of fruits and their states, as the experimental results show that FRYOLO achieves 84.7% in recall and 89.0% in mean Average Precision (mAP), along with a precision of 92.5%. In addition, it provides a detection frame rate of up to 33 Frames Per Second (FPS), satisfying the real-time requirement. Finally, an intelligent production line system based on FRYOLO is implemented, which not only provides robust technical support for the efficient operation of fruit production processes but also demonstrates the availability of the proposed network model in practical IoT applications. |
| format | Article |
| id | doaj-art-a214fa919fd9457d910b2a000f7cc66d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a214fa919fd9457d910b2a000f7cc66d2025-02-02T12:18:03ZengNature PortfolioScientific Reports2045-23222025-01-0115111710.1038/s41598-025-88439-wAn optimized lightweight real-time detection network model for IoT embedded devicesRongjun Chen0Peixian Wang1Binfan Lin2Leijun Wang3Xianxian Zeng4Xianglei Hu5Jun Yuan6Jiawen Li7Jinchang Ren8Huimin Zhao9School of Computer Science, Guangdong Polytechnic Normal UniversitySchool of Computer Science, Guangdong Polytechnic Normal UniversitySchool of Computer Science, Guangdong Polytechnic Normal UniversitySchool of Computer Science, Guangdong Polytechnic Normal UniversitySchool of Computer Science, Guangdong Polytechnic Normal UniversitySchool of Computer Science, Guangdong Polytechnic Normal UniversitySchool of Computer Science, Guangdong Polytechnic Normal UniversitySchool of Computer Science, Guangdong Polytechnic Normal UniversitySchool of Computer Science, Guangdong Polytechnic Normal UniversitySchool of Computer Science, Guangdong Polytechnic Normal UniversityAbstract With the rapid development of Internet of Things (IoT) technology, embedded devices in various computer vision scenarios can realize real-time target detection and recognition tasks, such as intelligent manufacturing, automatic driving, smart home, and so on. YOLOv8, as an advanced deep learning model in the field of target detection, has attracted much attention for its excellent detection speed, high precision, and multi-task processing capability. However, since IoT embedded devices typically own limited computing resources, direct deployment of YOLOv8 is a big challenge, especially for real-time detection tasks. To address this vital issue, this work proposes and deploys an optimized lightweight real-time detection network model that well-suits for IoT embedded devices, denoted as FRYOLO. To evaluate its performance, a case study based on real-time fresh and defective fruit detection in the production line is performed. Characterized by low training cost and high detection performance, this model accurately detects various types of fruits and their states, as the experimental results show that FRYOLO achieves 84.7% in recall and 89.0% in mean Average Precision (mAP), along with a precision of 92.5%. In addition, it provides a detection frame rate of up to 33 Frames Per Second (FPS), satisfying the real-time requirement. Finally, an intelligent production line system based on FRYOLO is implemented, which not only provides robust technical support for the efficient operation of fruit production processes but also demonstrates the availability of the proposed network model in practical IoT applications.https://doi.org/10.1038/s41598-025-88439-wYOLOv8Neural networksEmbedded deviceIoTComputer vision |
| spellingShingle | Rongjun Chen Peixian Wang Binfan Lin Leijun Wang Xianxian Zeng Xianglei Hu Jun Yuan Jiawen Li Jinchang Ren Huimin Zhao An optimized lightweight real-time detection network model for IoT embedded devices Scientific Reports YOLOv8 Neural networks Embedded device IoT Computer vision |
| title | An optimized lightweight real-time detection network model for IoT embedded devices |
| title_full | An optimized lightweight real-time detection network model for IoT embedded devices |
| title_fullStr | An optimized lightweight real-time detection network model for IoT embedded devices |
| title_full_unstemmed | An optimized lightweight real-time detection network model for IoT embedded devices |
| title_short | An optimized lightweight real-time detection network model for IoT embedded devices |
| title_sort | optimized lightweight real time detection network model for iot embedded devices |
| topic | YOLOv8 Neural networks Embedded device IoT Computer vision |
| url | https://doi.org/10.1038/s41598-025-88439-w |
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