A CNN-based model for low-textured object detection and tracking

This paper presents a specialized Single Shot Multi-box Detector (SSD) [9] tailored to the unique challenges of detecting low-textured objects in complex scenes. We harness the Fruit 360 dataset [10], featuring low-textured images of various fruits and vegetables, for both training and validation purposes. Our primary objective is to enable the integration of this streamlined SSD model [9] into mobile devices for the enhancement of mixed and augmented reality experiences. Remarkably, our custom model delivers a fourfold increase in processing speed compared to the original SSD [9], all while preserving or even enhancing detection accuracy on the designated dataset. Implementation is carried out in Python using TensorFlow