A CNN-based model for low-textured object detection and tracking - Université d'Évry Access content directly
Conference Papers Year : 2023

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

Abstract

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
No file

Dates and versions

hal-04478763 , version 1 (26-02-2024)

Identifiers

Cite

Salah-Eddine Laidoudi, Madjid Maidi, Samir Otmane. A CNN-based model for low-textured object detection and tracking. 19th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP 2023), Oct 2023, Cluj-Napoca, Romania. pp.221--227, ⟨10.1109/ICCP60212.2023.10398729⟩. ⟨hal-04478763⟩
26 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More