Xia Zilin, Gu Jinan, Wang Wenbo, Huang Zedong
School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China.
Math Biosci Eng. 2023 Nov 22;20(12):20971-20994. doi: 10.3934/mbe.2023928.
As an essential part of electronic component assembly, it is crucial to rapidly and accurately detect electronic components. Therefore, a lightweight electronic component detection method based on knowledge distillation is proposed in this study. First, a lightweight student model was constructed. Then, we consider issues like the teacher and student's differing expressions. A knowledge distillation method based on the combination of feature and channel is proposed to learn the teacher's rich class-related and inter-class difference features. Finally, comparative experiments were analyzed for the dataset. The results show that the student model Params (13.32 M) are reduced by 55%, and FLOPs (28.7 GMac) are reduced by 35% compared to the teacher model. The knowledge distillation method based on the combination of feature and channel improves the student model's mAP by 3.91% and 1.13% on the Pascal VOC and electronic components detection datasets, respectively. As a result of the knowledge distillation, the constructed student model strikes a superior balance between model precision and complexity, allowing for fast and accurate detection of electronic components with a detection precision (mAP) of 97.81% and a speed of 79 FPS.
作为电子元件装配的重要组成部分，快速准确地检测电子元件至关重要。因此，本研究提出了一种基于知识蒸馏的轻量化电子元件检测方法。首先，构建了一个轻量化的学生模型。然后，考虑教师和学生表达不同等问题。提出了一种基于特征和通道相结合的知识蒸馏方法，以学习教师丰富的类相关和类间差异特征。最后，对数据集进行了对比实验分析。结果表明，与教师模型相比，学生模型的参数（13.32M）减少了55%，浮点运算次数（28.7GMac）减少了35%。基于特征和通道相结合的知识蒸馏方法在Pascal VOC和电子元件检测数据集上分别将学生模型的平均精度均值（mAP）提高了3.91%和1.13%。经过知识蒸馏，构建的学生模型在模型精度和复杂度之间达到了更好的平衡，能够以97.81%的检测精度（mAP）和79帧每秒的速度快速准确地检测电子元件。
