中圖分類號(hào)： TP399 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.222837 中文引用格式： 張穎，張喆，龍光利. 融合輕量化與梯形結(jié)構(gòu)的學(xué)生行為檢測(cè)算法[J].電子技術(shù)應(yīng)用，2022，48(12)：47-53. 英文引用格式： Zhang Ying，Zhang Zhe，Long Guangli. Student behavior detection algorithm combining lightweight and trapezoidal structure[J]. Application of Electronic Technique，2022，48(12)：47-53.
Student behavior detection algorithm combining lightweight and trapezoidal structure
Zhang Ying1，Zhang Zhe1，Long Guangli2
1.School of Automation and Information Engineering，Xi′an University of Technology，Xi′an 710048，China； 2.School of Physics and Telecommunications Engineering，Shaanxi University of Technology，Hanzhong 723000，China
Abstract： In order to solve the problem that common target detection algorithms are difficult to apply effectively in classroom scenarios, a student behavior detection algorithm combining lightweight and trapezoidal structure is proposed. The algorithm is based on YOLOv4 architecture, according to the characteristics of target classification and distribution space, a new “trapezoidal” feature fusion structure is proposed, and combined with the MobileNetv2 idea, the model parameters are optimized to obtain a trapezoidal-MobileDarknet19 feature extraction network, which not only reduces the computational load of the network, but also improves the work efficiency. At the same time, it strengthens the information transmission of target features and improves the learning ability of the model. In the scale detection stage, a five-layer DenseNet network is introduced to enhance the network′s detection ability for small targets. The experimental results show that the proposed YOLOv4-ST algorithm is better than the original one. The mAP of YOLOv4 algorithm is improved by 5.5%. Compared with other mainstream algorithms, it has better practicability in the task of student classroom behavior detection.
Key words : trapezoidal structure；student behavior detection；YOLOv4；feature fusion；DenseNet
