{"title":"Csb-yolo: a rapid and efficient real-time algorithm for classroom student behavior detection","authors":"Wenqi Zhu, Zhijun Yang","doi":"10.1007/s11554-024-01515-8","DOIUrl":null,"url":null,"abstract":"<p>In recent years, the integration of artificial intelligence in education has become key to enhancing the quality of teaching. This study addresses the real-time detection of student behavior in classroom environments by proposing the Classroom Student Behavior YOLO (CSB-YOLO) model. We enhance the model’s multi-scale feature fusion capability using the Bidirectional Feature Pyramid Network (BiFPN). Additionally, we have designed a novel Efficient Re-parameterized Detection Head (ERD Head) to accelerate the model’s inference speed and introduced Self-Calibrated Convolutions (SCConv) to compensate for any potential accuracy loss resulting from lightweight design. To further optimize performance, model pruning and knowledge distillation are utilized to reduce the model size and computational demands while maintaining accuracy. This makes CSB-YOLO suitable for deployment on low-performance classroom devices while maintaining robust detection capabilities. Tested on the classroom student behavior dataset SCB-DATASET3, the distilled and pruned CSB-YOLO, with only 0.72M parameters and 4.3 Giga Floating-point Operations Per Second (GFLOPs), maintains high accuracy and exhibits excellent real-time performance, making it particularly suitable for educational environments.</p>","PeriodicalId":51224,"journal":{"name":"Journal of Real-Time Image Processing","volume":"1 1","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Real-Time Image Processing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11554-024-01515-8","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, the integration of artificial intelligence in education has become key to enhancing the quality of teaching. This study addresses the real-time detection of student behavior in classroom environments by proposing the Classroom Student Behavior YOLO (CSB-YOLO) model. We enhance the model’s multi-scale feature fusion capability using the Bidirectional Feature Pyramid Network (BiFPN). Additionally, we have designed a novel Efficient Re-parameterized Detection Head (ERD Head) to accelerate the model’s inference speed and introduced Self-Calibrated Convolutions (SCConv) to compensate for any potential accuracy loss resulting from lightweight design. To further optimize performance, model pruning and knowledge distillation are utilized to reduce the model size and computational demands while maintaining accuracy. This makes CSB-YOLO suitable for deployment on low-performance classroom devices while maintaining robust detection capabilities. Tested on the classroom student behavior dataset SCB-DATASET3, the distilled and pruned CSB-YOLO, with only 0.72M parameters and 4.3 Giga Floating-point Operations Per Second (GFLOPs), maintains high accuracy and exhibits excellent real-time performance, making it particularly suitable for educational environments.
期刊介绍:
Due to rapid advancements in integrated circuit technology, the rich theoretical results that have been developed by the image and video processing research community are now being increasingly applied in practical systems to solve real-world image and video processing problems. Such systems involve constraints placed not only on their size, cost, and power consumption, but also on the timeliness of the image data processed.
Examples of such systems are mobile phones, digital still/video/cell-phone cameras, portable media players, personal digital assistants, high-definition television, video surveillance systems, industrial visual inspection systems, medical imaging devices, vision-guided autonomous robots, spectral imaging systems, and many other real-time embedded systems. In these real-time systems, strict timing requirements demand that results are available within a certain interval of time as imposed by the application.
It is often the case that an image processing algorithm is developed and proven theoretically sound, presumably with a specific application in mind, but its practical applications and the detailed steps, methodology, and trade-off analysis required to achieve its real-time performance are not fully explored, leaving these critical and usually non-trivial issues for those wishing to employ the algorithm in a real-time system.
The Journal of Real-Time Image Processing is intended to bridge the gap between the theory and practice of image processing, serving the greater community of researchers, practicing engineers, and industrial professionals who deal with designing, implementing or utilizing image processing systems which must satisfy real-time design constraints.