Machine learning approach for prediction model on biomass characteristic analysis

Naryanto, Rizqi Fitri; Delimayanti, Mera Kartika

doi:10.1063/5.0115617

Cited by 1 publication

(1 citation statement)

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“…Precision quantifies the number of true positive instances among the predicted positives. The F1 score is the harmonic mean of precision and recall, providing a balance between these two metrics [29]. Accuracy, on the other hand, gives us a holistic view of the overall correctness of the model.…”

Section: Discussionmentioning

confidence: 99%

Efficient packaging defect detection: leveraging pre-trained vision models through transfer learning

Prastiwinarti,

Delimayanti,

Kurniawan

et al. 2024

IJEECS

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The inspection of packaging defects is a crucial aspect of maintaining the quality of industrial production, especially in the case of boxed products. This study introduces a novel approach for detecting physical defects in product packaging boxes by integrating image processing with deep learning, specifically transfer learning with two images as an input. The proposed method utilizes both top view and side view images of the packaging to determine its condition, a significant departure from the conventional single image input. Our approach incorporates 16 pre-trained model variants from EfficientNetV2, MobileNetV3, and ResNetV2 for transfer learning as feature extractors. The experimental findings demonstrate that the best model that leverages EfficientNetV2 variant achieves 100% accuracy and F1 score in terms of classification performance. However, the most optimal model in terms of classification performance and inference speed was the one that leveraged ResNetV2 variant. This model scored 95% accuracy and 95.24% F1 score, with an inference speed of 91 ms per prediction.

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Section: Discussionmentioning

confidence: 99%