Enhancing the accuracy of object recognition model on smart edge devices - Luận, Lê Chí; Thiên, Tô Hải (Journal of Science and Technology on Information security, 2023)
Abstract—Object recognition is one of the main topics in the AI ​​field. There are many AI models with high accuracy running well on high-configuration devices. However, smart edge devices (SED) are being widely used in many different fields because of their compact flexibility, ensuring personal data policy. Their limitation is hardware that only runs or supports 8bits 16bits or 32bits models. Therefore, running the model on SED must do the swap step (“quantization”). This also causes the recognition models to be significantly reduced in accuracy. In this paper, we propose the solution “GreedyPlus” – to capture high resolution frame (skip blur frame) and search for small objects in the image by cutting frames into small windows. Then, the solution zooms in and identifies objects. The last step determines the number of objects in the frame exactly. The method is simple but highly effective, improving the recognition results for the model significantly without the need to retrain the model with a new dataset. The results are tested and demonstrated on the datasets KITTI, CrownAI, and Autti.