Image recognition using artificial intelligence (human-object interaction detection in workplace)

The rapid evolution in deep learning for Computer Vision has accelerated the application of image analytics in many of our daily and work lives. In the Smart Factory context, image analytics can enable a more effective production monitoring process by interpreting the activities in the scenarios, pl...

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Main Author: Lin, Sining
Other Authors: Yap Kim Hui
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
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Online Access:https://hdl.handle.net/10356/157460
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1574602023-07-07T19:20:30Z Image recognition using artificial intelligence (human-object interaction detection in workplace) Lin, Sining Yap Kim Hui School of Electrical and Electronic Engineering EKHYap@ntu.edu.sg Engineering::Electrical and electronic engineering The rapid evolution in deep learning for Computer Vision has accelerated the application of image analytics in many of our daily and work lives. In the Smart Factory context, image analytics can enable a more effective production monitoring process by interpreting the activities in the scenarios, playing a vital role in digital transformation. In recent years, a new rising field, Human-Object Interaction (HOI) detection, is established to progress image analytics for semantic understanding. Given its capability of comprehending the interaction relationship between a specific human-object pair, the project specifically focused on applying the HOI detection to learn and recognize the production activities in the factory workplace. The project built a new industrial dataset, the Human Interaction with Factory Objects (HIFO) dataset, to introduce 8 factory objects with 9 sets of new interaction categories that commonly occur in the context. A two-stage Spatially Conditioning Graph (SCG) model, distinguished by combining spatial and appearance features, was used to perform the HOI detection in the new workplace scenarios. The project used transfer learning, and implemented proposed improvement techniques including negative instance suppression and Online Hard Example Mining (OHEM) loss function, to fine-tune the pre-trained SCG model on the dataset. The best performance achieved 61.51% mAP for the test dataset. Lastly, a graphical user interface (GUI) was designed for user-ends to visualize the results of the detections. In the last part of the report, conclusions were drawn from the discussion of quantitative and qualitative evaluations. Some recommendations were proposed to improve the SCG model detection in the factory workplace settings in future work. Overall, the SCG model achieved strong performance in HOI detection in the new proposed industrial dataset, implicating its practical applications for the Smart Factory context. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-19T05:26:48Z 2022-05-19T05:26:48Z 2022 Final Year Project (FYP) Lin, S. (2022). Image recognition using artificial intelligence (human-object interaction detection in workplace). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157460 https://hdl.handle.net/10356/157460 en A3297-211 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Lin, Sining
Image recognition using artificial intelligence (human-object interaction detection in workplace)
description The rapid evolution in deep learning for Computer Vision has accelerated the application of image analytics in many of our daily and work lives. In the Smart Factory context, image analytics can enable a more effective production monitoring process by interpreting the activities in the scenarios, playing a vital role in digital transformation. In recent years, a new rising field, Human-Object Interaction (HOI) detection, is established to progress image analytics for semantic understanding. Given its capability of comprehending the interaction relationship between a specific human-object pair, the project specifically focused on applying the HOI detection to learn and recognize the production activities in the factory workplace. The project built a new industrial dataset, the Human Interaction with Factory Objects (HIFO) dataset, to introduce 8 factory objects with 9 sets of new interaction categories that commonly occur in the context. A two-stage Spatially Conditioning Graph (SCG) model, distinguished by combining spatial and appearance features, was used to perform the HOI detection in the new workplace scenarios. The project used transfer learning, and implemented proposed improvement techniques including negative instance suppression and Online Hard Example Mining (OHEM) loss function, to fine-tune the pre-trained SCG model on the dataset. The best performance achieved 61.51% mAP for the test dataset. Lastly, a graphical user interface (GUI) was designed for user-ends to visualize the results of the detections. In the last part of the report, conclusions were drawn from the discussion of quantitative and qualitative evaluations. Some recommendations were proposed to improve the SCG model detection in the factory workplace settings in future work. Overall, the SCG model achieved strong performance in HOI detection in the new proposed industrial dataset, implicating its practical applications for the Smart Factory context.
author2 Yap Kim Hui
author_facet Yap Kim Hui
Lin, Sining
format Final Year Project
author Lin, Sining
author_sort Lin, Sining
title Image recognition using artificial intelligence (human-object interaction detection in workplace)
title_short Image recognition using artificial intelligence (human-object interaction detection in workplace)
title_full Image recognition using artificial intelligence (human-object interaction detection in workplace)
title_fullStr Image recognition using artificial intelligence (human-object interaction detection in workplace)
title_full_unstemmed Image recognition using artificial intelligence (human-object interaction detection in workplace)
title_sort image recognition using artificial intelligence (human-object interaction detection in workplace)
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/157460
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