Real-Time Detection of Low-Textured Objects based on Deep Learning

被引：0
|
作者：
Laidoudi, Salah-eddine [1 ,2 ]
Maidi, Madjid [1 ,2 ]
Otmane, Samir [1 ]
机构：
[1] Univ Evry, Univ Paris Saclay, IBISC, F-91020 Evry Courcouronnes, France
[2] ESME, ESME Res Lab, 38 Rue Moliere, Ivry, France
来源：
2023 IEEE 25TH INTERNATIONAL WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING, MMSP | 2023年
关键词：
Custom SSD (Single Shot multi-box Detector); Fruit; 360; dataset; Low textured objects; CNN; Mixed Reality; Augmented Reality; !text type='Python']Python[!/text;
D O I：
10.1109/MMSP59012.2023.10337653
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
In this paper, a custom Single Shot Multi-box Detector (SSD) [1] is proposed for object detection on difficult scenes. The fruit 360 dataset [2], with low-textured images of different fruits and vegetables, is used as a training and validation data set. The purpose of this research is to implement the detector on mobile devices for mixed and augmented reality experiences, so a lighter weight SSD [1] model was designed while retaining its performance. The custom model is 4 times faster than the original SSD [1] model and the tests showed that it is even more accurate on the designated data set. The model is implemented in Python using Tensorflow and will soon be available on GitHub for public use.
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页数：6
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