Real Time Switch Object Detection Model Based on Knowledge Distillation for Edge Computing

With the improvement of Information Technology, it is necessary to detect the equipment target in real time on the end side. Although the existing lightweight target detection models achieve the balance of accuracy and speed to a certain extent, they are generally not effective in dense small target recognition, and strengthening the detailed features directly through the method of feature fusion will destroy the lightweight attribute. Some existing target detection models which are suitable for multiple scenes, have good generalization performance and high precision, and can not meet the reasoning requirements of end-to-side deployment in speed. Therefore, aiming at the actual situation that the data distribution of vertical scene is relatively simple, this paper successfully learns the lightweight model for target detection of distribution cabinet switches by means of knowledge distillation, using centernet model based on hourglass-l04 as teacher and centernet model based on hourglass-52 as student. The experimental results show that the model can quickly and accurately detect the switching components of the distribution cabinet at the end side. Compared with the existing model, the detection speed is about twice as fast while maintaining high-precision detection.