A Comparative Analysis of Cross-Validation Techniques for a Smart and Lean Pick-and-Place Solution with Deep Learning

Kee, Elven; Chong, Jun Jie; Choong, Zi Jie; Lau, Michael

doi:10.3390/electronics12112371

Cited by 15 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In comparison to other classes, the yellow cylinder has the lowest AP at 53.8% and the highest standard deviation at 8.69%. This is consistent with our prior findings [26], which show that neutralcolored things such as yellow cubes have lower APs than strong-colored objects such as red cubes. As the objects for this pick-and-place action have a metallic surface, yellow objects suffer from surface reflection from external lighting.…”

Section: Results Of Variation Of Batch Size On Average Precisionsupporting

confidence: 93%

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

Kee,

Chong,

Choong

et al. 2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Results Of Variation Of Batch Size On Average Precisionsupporting

confidence: 93%

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

Kee,

Chong,

Choong

et al. 2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…This study is a continuation of our published works [18,19]. Figure 2 depicts the project setup for a smart and lean pick-and-place operation.…”

Section: Methodsmentioning

confidence: 94%

“…This is similar to Charloke [31], who used CNN on Raspberry Pi to monitor a codling moth population and achieve a high accuracy of 99% with a small dataset of 430 images. In addition, a small dataset allows for fast training time and practical data preparation [18]. Figure 5 illustrates the effect of the RGB saturation level on the images.…”

Section: Image Enhancementmentioning

confidence: 99%

Object Detection with Hyperparameter and Image Enhancement Optimisation for a Smart and Lean Pick-and-Place Solution

Kee,

Chong,

Choong

et al. 2024

Signals

Self Cite

View full text Add to dashboard Cite

Pick-and-place operations are an integral part of robotic automation and smart manufacturing. By utilizing deep learning techniques on resource-constraint embedded devices, the pick-and-place operations can be made more accurate, efficient, and sustainable, compared to the high-powered computer solution. In this study, we propose a new technique for object detection on an embedded system using SSD Mobilenet V2 FPN Lite with the optimisation of the hyperparameter and image enhancement. By increasing the Red Green Blue (RGB) saturation level of the images, we gain a 7% increase in mean Average Precision (mAP) when compared to the control group and a 20% increase in mAP when compared to the COCO 2017 validation dataset. Using a Learning Rate of 0.08 with an Edge Tensor Processing Unit (TPU), we obtain high real-time detection scores of 97%. The high detection scores are important to the control algorithm, which uses the bounding box to send a signal to the collaborative robot for pick-and-place operation.

show abstract

“…We used Precision, Recall, mean average precision (mAP), and F1 score to evaluate the model's performance. A validation test employing a tenfold cross-validation method was applied to calculate the evaluation metrics, and the performance metric for the model was the average of the tenfold cross-validation data [63].…”

Section: Performance Evaluationmentioning

confidence: 99%

Intelligent Detection of Marine Offshore Aquaculture with High-Resolution Optical Remote Sensing Images

Dong,

Shi,

Hao

et al. 2024

JMSE

View full text Add to dashboard Cite

The rapid and disordered expansion of artificial marine aquaculture areas has caused severe ecological and environmental problems. Accurate monitoring of offshore aquaculture areas is urgent and significant in order to support the scientific and sustainable management and protection of coastal marine resources. Artificial intelligence provides a valuable tool to improve marine resource monitoring. Deep learning methods have been widely used for marine object detection, but You Only Look Once (YOLO) models have not been employed for offshore aquaculture area monitoring. This study therefore evaluated the capacity of two well-known YOLO models, YOLOv5 and YOLOv7, to detect offshore aquaculture areas based on different high-resolution optical remote sensing imagery. Compared with YOLOv7 based on a satellite dataset, YOLOv5 increased the Precision value by approximately 3.29% (to 95.33%), Recall value by 3.02% (to 93.02%), mAP_0.5 by 2.03% (to 96.22%), and F1 score by 2.65% (to 94.16%). Based on the Google Earth dataset, YOLOv5 and YOLOv7 showed similar results. We found that the spatial resolution could affect the deep learning models’ performances. We used the Real-ESRGAN method to enhance the spatial resolution of satellite dataset and investigated whether super-resolution (SR) methods improved the detection accuracy of the YOLO models. The results indicated that despite improving the image clarity and resolution, the SR methods negatively affected the performance of the YOLO models for offshore aquaculture object detection. This suggests that attention should be paid to the use of SR methods before the application of deep learning models for object detection using remote sensing imagery.

show abstract

A Comparative Analysis of Cross-Validation Techniques for a Smart and Lean Pick-and-Place Solution with Deep Learning

Cited by 15 publications

References 23 publications

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

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

Object Detection with Hyperparameter and Image Enhancement Optimisation for a Smart and Lean Pick-and-Place Solution

Intelligent Detection of Marine Offshore Aquaculture with High-Resolution Optical Remote Sensing Images

Contact Info

Product

Resources

About