Visual Data Acquisition for Measuring Devices using Deep Learning

2021 ◽  
Author(s):  
Youssef Abdelrahman ◽  
Mostafa El-Salamony ◽  
Mohamed Khalifa
Keyword(s):  
Deep Learning ◽  
Data Acquisition ◽  
Visual Data ◽  
Measuring Devices

scholarly journals A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone

Drones ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Apostolos Papakonstantinou ◽  
Marios Batsaris ◽  
Spyros Spondylidis ◽  
Konstantinos Topouzelis
Keyword(s):  
Deep Learning ◽  
Data Acquisition ◽  
Citizen Science ◽  
Coastal Zone ◽  
Automatic Detection ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Marine Litter ◽  
Density Maps ◽  
Learning Techniques

Marine litter (ML) accumulation in the coastal zone has been recognized as a major problem in our time, as it can dramatically affect the environment, marine ecosystems, and coastal communities. Existing monitoring methods fail to respond to the spatiotemporal changes and dynamics of ML concentrations. Recent works showed that unmanned aerial systems (UAS), along with computer vision methods, provide a feasible alternative for ML monitoring. In this context, we proposed a citizen science UAS data acquisition and annotation protocol combined with deep learning techniques for the automatic detection and mapping of ML concentrations in the coastal zone. Five convolutional neural networks (CNNs) were trained to classify UAS image tiles into two classes: (a) litter and (b) no litter. Testing the CCNs’ generalization ability to an unseen dataset, we found that the VVG19 CNN returned an overall accuracy of 77.6% and an f-score of 77.42%. ML density maps were created using the automated classification results. They were compared with those produced by a manual screening classification proving our approach’s geographical transferability to new and unknown beaches. Although ML recognition is still a challenging task, this study provides evidence about the feasibility of using a citizen science UAS-based monitoring method in combination with deep learning techniques for the quantification of the ML load in the coastal zone using density maps.

IoT Data Acquisition Node For Deep Learning Time Series Prediction

2021 ◽  
Author(s):  
Li Xinyun ◽  
Liu Huidan ◽  
Yin Hang ◽  
Cao Zilan ◽  
Chen Bangdi ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Data Acquisition ◽  
Time Series Prediction ◽  
Learning Time

scholarly journals Application of region-based video surveillance in smart cities using deep learning

2021 ◽  
Author(s):  
Asma Zahra ◽  
Mubeen Ghafoor ◽  
Kamran Munir ◽  
Ata Ullah ◽  
Zain Ul Abideen
Keyword(s):  
Deep Learning ◽  
Video Coding ◽  
Video Surveillance ◽  
Smart City ◽  
Smart Cities ◽  
Video Frame ◽  
Visual Data ◽  
High Quality ◽  
City Environment ◽  
Salient Regions

AbstractSmart video surveillance helps to build more robust smart city environment. The varied angle cameras act as smart sensors and collect visual data from smart city environment and transmit it for further visual analysis. The transmitted visual data is required to be in high quality for efficient analysis which is a challenging task while transmitting videos on low capacity bandwidth communication channels. In latest smart surveillance cameras, high quality of video transmission is maintained through various video encoding techniques such as high efficiency video coding. However, these video coding techniques still provide limited capabilities and the demand of high-quality based encoding for salient regions such as pedestrians, vehicles, cyclist/motorcyclist and road in video surveillance systems is still not met. This work is a contribution towards building an efficient salient region-based surveillance framework for smart cities. The proposed framework integrates a deep learning-based video surveillance technique that extracts salient regions from a video frame without information loss, and then encodes it in reduced size. We have applied this approach in diverse case studies environments of smart city to test the applicability of the framework. The successful result in terms of bitrate 56.92%, peak signal to noise ratio 5.35 bd and SR based segmentation accuracy of 92% and 96% for two different benchmark datasets is the outcome of proposed work. Consequently, the generation of less computational region-based video data makes it adaptable to improve surveillance solution in Smart Cities.

scholarly journals Ani-GIFs: A Benchmark Dataset for Domain Generalization of Action Recognition from GIFs

2021 ◽  
Author(s):  
Shoumik Majumdar ◽  
Shubhangi Jain ◽  
Isidora Chara Tourni ◽  
Arsenii Mustafin ◽  
Diala Lteif ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Acquisition ◽  
Action Recognition ◽  
Data Augmentation ◽  
Learning Models ◽  
Visual Content ◽  
Lack Of Information ◽  
Temporal Features ◽  
Synthetic Video ◽  
Multiple Domains

Deep learning models perform remarkably well for the same task under the assumption that data is always coming from the same distribution. However, this is generally violated in practice, mainly due to the differences in the data acquisition techniques and the lack of information about the underlying source of new data. Domain Generalization targets the ability to generalize to test data of an unseen domain; while this problem is well-studied for images, such studies are significantly lacking in spatiotemporal visual content – videos and GIFs. This is due to (1) the challenging nature of misalignment of temporal features and the varying appearance/motion of actors and actions in different domains, and (2) spatiotemporal datasets being laborious to collect and annotate for multiple domains. We collect and present the first synthetic video dataset of Animated GIFs for domain generalization, Ani-GIFs, that is used to study domain gap of videos vs. GIFs, and animated vs. real GIFs, for the task of action recognition. We provide a training and testing setting for Ani-GIFs, and extend two domain generalization baseline approaches, based on data augmentation and explainability, to the spatiotemporal domain to catalyze research in this direction.

scholarly journals A Data-Centric Approach to Design and Analysis of a Surface-Inspection System Based on Deep Learning in the Plastic Injection Molding Industry

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1895
Author(s):  
Donggyun Im ◽  
Sangkyu Lee ◽  
Homin Lee ◽  
Byungguan Yoon ◽  
Fayoung So ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Data Acquisition ◽  
Type I Error ◽  
Vision System ◽  
Structural Characteristics ◽  
Inspection System ◽  
Type I ◽  
Data Annotation ◽  
Background Removal

Manufacturers are eager to replace the human inspector with automatic inspection systems to improve the competitive advantage by means of quality. However, some manufacturers have failed to apply the traditional vision system because of constraints in data acquisition and feature extraction. In this paper, we propose an inspection system based on deep learning for a tampon applicator producer that uses the applicator’s structural characteristics for data acquisition and uses state-of-the-art models for object detection and instance segmentation, YOLOv4 and YOLACT for feature extraction, respectively. During the on-site trial test, we experienced some False-Positive (FP) cases and found a possible Type I error. We used a data-centric approach to solve the problem by using two different data pre-processing methods, the Background Removal (BR) and Contrast Limited Adaptive Histogram Equalization (CLAHE). We have experimented with analyzing the effect of the methods on the inspection with the self-created dataset. We found that CLAHE increased Recall by 0.1 at the image level, and both CLAHE and BR improved Precision by 0.04–0.06 at the bounding box level. These results support that the data-centric approach might improve the detection rate. However, the data pre-processing techniques deteriorated the metrics used to measure the overall performance, such as F1-score and Average Precision (AP), even though we empirically confirmed that the malfunctions improved. With the detailed analysis of the result, we have found some cases that revealed the ambiguity of the decisions caused by the inconsistency in data annotation. Our research alerts AI practitioners that validating the model based only on the metrics may lead to a wrong conclusion.

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