IoT Based Inventory Management System with Recipe Recommendation Using Collaborative Filtering

Devasthali, Atharva S.; Chaudhari, A.; Bhutada, Someshkumar S.; Doshi, Snehal R.; Suryawanshi, Vaishali P.

doi:10.1007/978-981-15-5258-8_50

Cited by 1 publication

(1 citation statement)

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“…The data coming from the physical environment are then sent to a web platform and processed, thus facilitating decision making, especially in process changes [3]. Concretely, IoT enables to develop the interconnectivity of the different tools and systems of the manufacturing chain by exploiting the intelligent sensors data using big data and artificial intelligence (AI), which include numerous advantages and benefits for main industrial operations such as manufacturing process automation and monitoring [4], predictive maintenance [5], resource and inventory management [6], and quality control [7].…”

Section: Introductionmentioning

confidence: 99%

Impact of Image Compression on the Performance of Steel Surface Defect Classification with a CNN

Benbarrad

Eloutouate

Arioua

et al. 2021

JSAN

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Machine vision is increasingly replacing manual steel surface inspection. The automatic inspection of steel surface defects makes it possible to ensure the quality of products in the steel industry with high accuracy. However, the optimization of inspection time presents a great challenge for the integration of machine vision in high-speed production lines. In this context, compressing the collected images before transmission is essential to save bandwidth and energy, and improve the latency of vision applications. The aim of this paper was to study the impact of quality degradation resulting from image compression on the classification performance of steel surface defects with a CNN. Image compression was applied to the Northeastern University (NEU) surface-defect database with various compression ratios. Three different models were trained and tested with these images to classify surface defects using three different approaches. The obtained results showed that trained and tested models on the same compression qualities maintained approximately the same classification performance for all used compression grades. In addition, the findings clearly indicated that the classification efficiency was affected when the training and test datasets were compressed using different parameters. This impact was more obvious when there was a large difference between these compression parameters, and for models that achieved very high accuracy. Finally, it was found that compression-based data augmentation significantly increased the classification precision to perfect scores (98–100%), and thus improved the generalization of models when tested on different compression qualities. The importance of this work lies in exploiting the obtained results to successfully integrate image compression into machine vision systems, and as appropriately as possible.

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