Methodology for generating synthetic labeled datasets for visual container inspection

Delgado, Guillem; Cortés, Andoni; García, Sara; Loyo, Estíbaliz; Berasategi, Maialen; Aranjuelo, Nerea

doi:10.1016/j.tre.2023.103174

Cited by 2 publications

(2 citation statements)

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“…In industrial contexts, synthetic data have been employed for tasks including segmenting car parts in the automotive industry [ 4 ], training robots for bin-picking chicken fillets in the food industry [ 5 ], and detecting surface defects on machine parts [ 6 , 7 , 8 ]. The generation of the synthetic images from 3D geometry is often carried out by using Unity [ 3 , 4 ], and sometimes custom solutions are put into place [ 7 , 9 ], whereas Blender is utilized in the majority of the studies [ 1 , 2 , 5 , 6 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…This is based on the assumption that a greater extent of domain randomization leads to enhanced domain adaptation and, thus, improved model performance for real images. While it’s common to alter at least some of the rendering parameters, particularly focusing on camera position and lighting, beyond this assumption, most studies do not delve deeper into the specific impacts of these variations [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 10 , 12 , 13 , 14 , 15 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

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Synthetic Training Data in AI-Driven Quality Inspection: The Significance of Camera, Lighting, and Noise Parameters

Schraml,

Notni

2024

Sensors

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Industrial-quality inspections, particularly those leveraging AI, require significant amounts of training data. In fields like injection molding, producing a multitude of defective parts for such data poses environmental and financial challenges. Synthetic training data emerge as a potential solution to address these concerns. Although the creation of realistic synthetic 2D images from 3D models of injection-molded parts involves numerous rendering parameters, the current literature on the generation and application of synthetic data in industrial-quality inspection scarcely addresses the impact of these parameters on AI efficacy. In this study, we delve into some of these key parameters, such as camera position, lighting, and computational noise, to gauge their effect on AI performance. By utilizing Blender software, we procedurally introduced the “flash” defect on a 3D model sourced from a CAD file of an injection-molded part. Subsequently, with Blender’s Cycles rendering engine, we produced datasets for each parameter variation. These datasets were then used to train a pre-trained EfficientNet-V2 for the binary classification of the “flash” defect. Our results indicate that while noise is less critical, using a range of noise levels in training can benefit model adaptability and efficiency. Variability in camera positioning and lighting conditions was found to be more significant, enhancing model performance even when real-world conditions mirror the controlled synthetic environment. These findings suggest that incorporating diverse lighting and camera dynamics is beneficial for AI applications, regardless of the consistency in real-world operational settings.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synthetic Training Data in AI-Driven Quality Inspection: The Significance of Camera, Lighting, and Noise Parameters

Schraml,

Notni

2024

Sensors

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Comparative Analysis of Deep Learning Models for Olive Detection on the Branch

Kahya,

Aslan

2024

Wseas Transactions on Computers

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The future of deep learning integration in agriculture holds great potential for advancing sustainable agricultural practices, precision agriculture and improved decision-making. With the rapid development of image processing and artificial intelligence technologies in recent years, deep learning has begun to play a major role in identifying agricultural pests and optimizing agricultural product marketing. However, there are challenges related to data quality, model scalability, and geographical limitations for widespread adoption of deep learning in agriculture. This study on Olive was conducted to improve the quality of the data set and to ensure more reliable training of object detection models. According to the result of the training process of YOLOv7 used in the study, it was concluded that it was characterized by decreasing loss values and showed an increase in the model's ability to detect objects correctly. It was observed that the other model, YOLOv8l, had a more effective learning capacity and a tendency to learn faster. The performance of both models was evaluated with various metrics, and it was determined that YOLOv8l had higher Precision, Recall, and mAP values. It was emphasized that YOLOv8l showed high performance even in low epoch numbers and can be preferred especially in cases where time and computational resources were limited. It was determined that YOLOv7 made detections in a wide confidence range, but had difficulty in detections with low confidence scores. It was observed that YOLOv8l made more stable and reliable detections with higher confidence scores. The metric data of the "YOLOv8l" model was found to be higher compared to other models. The F1 score of the YOLOv5l model was 92.337%, precision 96.568%, recall %88,462,mAP@0.5:0.65 value gave the highest score with 94.608%. This research on deep learning-based object detection models indicated that YOLOv8l showed superior performance compared to YOLOv7 and was a more reliable option for agricultural applications.

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Methodology for generating synthetic labeled datasets for visual container inspection

Cited by 2 publications

References 34 publications

Synthetic Training Data in AI-Driven Quality Inspection: The Significance of Camera, Lighting, and Noise Parameters

Synthetic Training Data in AI-Driven Quality Inspection: The Significance of Camera, Lighting, and Noise Parameters

Comparative Analysis of Deep Learning Models for Olive Detection on the Branch

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