Development of Smart and Lean Pick-and-Place System Using EfficientDet-Lite for Custom Dataset

Object detection for a pick-and-place system has been widely acknowledged as a significant research area in the field of computer vision. The integration of AI and machine vision with pick-and-place operations should be made affordable for Small and Medium Enterprises (SMEs) so they can leverage thi...

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Bibliographic Details
Published in:Applied sciences 2023-10, Vol.13 (20), p.11131
Main Authors: Kee, Elven, Chong, Jun Jie, Choong, Zi Jie, Lau, Michael
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Average Precision
Cameras
Datasets
Deep learning
Detectors
EfficientDet-Lite
Embedded systems
Machine learning
Machine vision
Manufacturing
Methods
object detection
Robots
Small & medium sized enterprises-SME
Small and medium sized companies
Trends
Vision systems
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Summary:Object detection for a pick-and-place system has been widely acknowledged as a significant research area in the field of computer vision. The integration of AI and machine vision with pick-and-place operations should be made affordable for Small and Medium Enterprises (SMEs) so they can leverage this technology. Therefore, the aim of this study is to develop a smart and lean pick-and-place solution for custom workpieces, which requires minimal computational resources. In this study, we evaluate the effectiveness of illumination and batch size to improve the Average Precision (AP) and detection score of an EfficientDet-Lite model. The addition of 8% optimized bright Alpha3 images results in an increase of 7.5% in AP and a 6.3% increase in F1-score as compared to the control dataset. Using a training batch size of 4, the AP is significantly improved to 66.8% as compared to a batch size of 16 at 57.4%. The detection scores are improved to 80% with a low variance of 1.65 using a uniform 135-angle lamp and 0 illumination level. The pick-and-place solution is validated using Single-Shot Detector (SSD) MobileNet V2 Feature Pyramid Network (FPN) Lite. Our experimental results clearly show that the proposed method has an increase of 5.19% in AP compared to SSD MobileNet V2 FPNLite.
ISSN:2076-3417
2076-3417
DOI:10.3390/app132011131