Instance Segmentation of Personal Protective Equipment using a Multi-stage Transfer Learning Process

Truong, Thomas; Bhatt, Aakash; Queiroz, Leonardo; Lai, Kenneth; Yanushkevich, Svetlana

doi:10.1109/smc42975.2020.9283427

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…Fig. 7 shows an example of the images provided by [23] and the instance segmentations provided by [24]. The Personal Protective Equipment (PPE) dataset contains the following instance segmented classes:…”

Section: ) Visual Relationship Evaluationmentioning

confidence: 99%

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Relatable Clothing: Soft-Attention Mechanism for Detecting Worn/Unworn Objects

Truong

Yanushkevich

2021

IEEE Access

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We have identified a need in visual relationship detection and biometrics related research for a dataset and model which focuses on person-clothing pairs. Previous to our Relatable Clothing dataset, there were no publicly available datasets usable for "worn" and "unworn" clothing detection. In this paper we propose a novel visual relationship model architecture for "worn" and "unworn" clothing detection that makes use of a soft attention mechanism for feature fusion between a conventional ResNet backbone and our novel person-clothing mask feature extraction architecture. The best proposed model achieves 98.62% accuracy, 99.50% precision, 98.31% recall, and 99.14% specificity on the Relatable Clothing dataset, outperforming our previous iterations. We release our models which can be found on the Relatable Clothing GitHub repository for future research and applications into detecting and analyzing person-clothing pairs.

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Section: ) Visual Relationship Evaluationmentioning

confidence: 99%

“…The PPE dataset contains only 110 images for training and 110 images for testing, making it a small dataset. Originally provided by [23] for bounding boxes only, we had provided instance segmentations in [24]. The dataset also does not contain person instance segmentations or visual relationship annotations.…”

Section: ) Visual Relationship Evaluationmentioning

confidence: 99%

Relatable Clothing: Soft-Attention Mechanism for Detecting Worn/Unworn Objects

Truong

Yanushkevich

2021

IEEE Access

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“…Image classification is an image-level visual recognition task that aims to classify each visual image into one of the pre-defined semantic categories; object detection is an instance-level visual recognition task that locates all the objects in a visual image and recognizes their semantic categories; semantic segmentation is a pixel-level visual recognition task that aims to assign a semantic category label to each and every pixel of an image. The progress in this research field enable a wide range of applications in computer vision, including autonomous vehicles [25][26][27][28][29][30], the analysis of medical images [31][32][33][34][35], the surveillance of manufacturing [37][38][39][40][41][42], construction [43][44][45][46], agriculture [47][48][49][50][51][52] and retail [53][54][55][56], and augmented and virtual reality in entertainment [57][58][59][60]. The technical methods of visual recognition can be broadly…”

Section: Visual Recognitionmentioning

confidence: 99%

“…object detection [21,22] and semantic segmentation [23,24]. In practice, visual recognition (i.e., classification, detection and segmentation) plays a significant role in various computer vision scenarios and applications including transportation (e.g., autonomous vehicles [25,26], drones [27,28] and robots [29,30]), healthcare (e.g., analysis of CT [31] and MRI [32,33] images, cancer detection [34,35] and patient movement analysis [36]), manufacturing (e.g., defect inspection [37,38], scene text recognition [39,40] and product assembly [41,42]), construction (e.g., predictive maintenance [43,44] and personal protective equipment detection [45,46]), agriculture (e.g., crop and livestock surveillance [47,48], automatic weeding [49,50] and insect detection [51,52]), retail (e.g., self-checkout [53,54] and surveillance for unmanned supermarkets [55,56]) and entertainment (e.g., augmented reality [57,58] and virtual reality [59,60]).…”

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confidence: 99%

Transductive transfer learning for visual recognition

Huang¹

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has given invaluable advice on almost all aspects of my Ph.D. study. I would also express great gratitude towards all the lab-mates in MICL, CSL and SCALE Labs and all the members in Prof Lu's research group for the enthusiastic discussions and priceless support in the last four years. Dayan, Aoran and Pengdeng brought me a lot of precious memory in my Ph.D. journey. Thank Jingyi, Kaiwen, Zichen, Yun and Xueying for their hardworking while we collaborated on projects and researches. I enjoy the good works we have done and the team-working time.Thanks to all of my thesis committee members, Prof. Lin Guosheng, Prof. Liu Ziwei and Prof. Chen Change Loy for the time, kind suggestions and feedback for my qualification examination and thesis. You have all been very patient and kind throughout the whole process, and your encouragement and suggestions have helped refine my work greatly.Last but most importantly, I wish to express the deepest gratitude to my family for their constant and unconditional love and support. Thank you for being always there for me, for listening, for enlightening and for giving me the strength and courage to overcome difficulties and pursue greatness.xi

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A systematic review of computer vision-based personal protective equipment compliance in industry practice: advancements, challenges and future directions

Vukicevic,

Petrovic,

Milosevic

et al. 2024

Artif Intell Rev

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Computerized compliance of Personal Protective Equipment (PPE) is an emerging topic in academic literature that aims to enhance workplace safety through the automation of compliance and prevention of PPE misuse (which currently relies on manual employee supervision and reporting). Although trends in the scientific literature indicate a high potential for solving the compliance problem by employing computer vision (CV) techniques, the practice has revealed a series of barriers that limit their wider applications. This article aims to contribute to the advancement of CV-based PPE compliance by providing a comparative review of high-level approaches, algorithms, datasets, and technologies used in the literature. The systematic review highlights industry-specific challenges, environmental variations, and computational costs related to the real-time management of PPE compliance. The issues of employee identification and identity management are also discussed, along with ethical and cybersecurity concerns. Through the concept of CV-based PPE Compliance 4.0, which encapsulates PPE, human, and company spatio-temporal variabilities, this study provides guidelines for future research directions for addressing the identified barriers. The further advancements and adoption of CV-based solutions for PPE compliance will require simultaneously addressing human identification, pose estimation, object recognition and tracking, necessitating the development of corresponding public datasets.

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Instance Segmentation of Personal Protective Equipment using a Multi-stage Transfer Learning Process

Cited by 3 publications

References 11 publications

Relatable Clothing: Soft-Attention Mechanism for Detecting Worn/Unworn Objects

Relatable Clothing: Soft-Attention Mechanism for Detecting Worn/Unworn Objects

Transductive transfer learning for visual recognition

A systematic review of computer vision-based personal protective equipment compliance in industry practice: advancements, challenges and future directions

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