Hybrid Object Detection Using Domain-Specific Datasets

2021 ◽  
Author(s):  
Martin Stancel ◽  
Branislav Mados ◽  
Martin Chovanec ◽  
Peter Balaz
Keyword(s):  
Object Detection ◽  
Domain Specific

scholarly journals Domain-Specific Suppression for Adaptive Object Detection

2021 ◽  
Author(s):  
Yu Wang ◽  
Rui Zhang ◽  
Shuo Zhang ◽  
Miao Li ◽  
YangYang Xia ◽  
...  
Keyword(s):  
Object Detection ◽  
Specific Suppression ◽  
Domain Specific

scholarly journals Domain-Specific Approximation for Object Detection

IEEE Micro ◽  
2018 ◽  
Vol 38 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Ting-Wu Chin ◽  
Chia-Lin Yu ◽  
Matthew Halpern ◽  
Hasan Genc ◽  
Shiao-Li Tsao ◽  
...  
Keyword(s):  
Object Detection ◽  
Domain Specific

scholarly journals A Baseline for General Music Object Detection with Deep Learning

2018 ◽  
Vol 8 (9) ◽  
pp. 1488 ◽  
Author(s):  
Alexander Pacha ◽  
Jan Hajič ◽  
Jorge Calvo-Zaragoza
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Evaluation Criteria ◽  
Qualitative Comparison ◽  
Evaluation Standard ◽  
Detection Model ◽  
Detection Systems ◽  
Domain Specific ◽  
Symbol Detection ◽  
First Time

Deep learning is bringing breakthroughs to many computer vision subfields including Optical Music Recognition (OMR), which has seen a series of improvements to musical symbol detection achieved by using generic deep learning models. However, so far, each such proposal has been based on a specific dataset and different evaluation criteria, which made it difficult to quantify the new deep learning-based state-of-the-art and assess the relative merits of these detection models on music scores. In this paper, a baseline for general detection of musical symbols with deep learning is presented. We consider three datasets of heterogeneous typology but with the same annotation format, three neural models of different nature, and establish their performance in terms of a common evaluation standard. The experimental results confirm that the direct music object detection with deep learning is indeed promising, but at the same time illustrates some of the domain-specific shortcomings of the general detectors. A qualitative comparison then suggests avenues for OMR improvement, based both on properties of the detection model and how the datasets are defined. To the best of our knowledge, this is the first time that competing music object detection systems from the machine learning paradigm are directly compared to each other. We hope that this work will serve as a reference to measure the progress of future developments of OMR in music object detection.

scholarly journals ABOships—An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations

2021 ◽  
Vol 13 (5) ◽  
pp. 988
Author(s):  
Bogdan Iancu ◽  
Valentin Soloviev ◽  
Luca Zelioli ◽  
Johan Lilius
Keyword(s):  
Object Detection ◽  
The Other ◽  
Object Size ◽  
Atmospheric Conditions ◽  
Large Object ◽  
Detection Algorithms ◽  
Domain Specific ◽  
Vessel Detection ◽  
Feature Extractor ◽  
Essential Problem

Availability of domain-specific datasets is an essential problem in object detection. Datasets of inshore and offshore maritime vessels are no exception, with a limited number of studies addressing maritime vessel detection on such datasets. For that reason, we collected a dataset consisting of images of maritime vessels taking into account different factors: background variation, atmospheric conditions, illumination, visible proportion, occlusion and scale variation. Vessel instances (including nine types of vessels), seamarks and miscellaneous floaters were precisely annotated: we employed a first round of labelling and we subsequently used the CSRT tracker to trace inconsistencies and relabel inadequate label instances. Moreover, we evaluated the out-of-the-box performance of four prevalent object detection algorithms (Faster R-CNN, R-FCN, SSD and EfficientDet). The algorithms were previously trained on the Microsoft COCO dataset. We compared their accuracy based on feature extractor and object size. Our experiments showed that Faster R-CNN with Inception-Resnet v2 outperforms the other algorithms, except in the large object category where EfficientDet surpasses the latter.

scholarly journals Domain-Specific On-Device Object Detection Method

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 77
Author(s):  
Seongju Kang ◽  
Jaegi Hwang ◽  
Kwangsue Chung
Keyword(s):  
Object Detection ◽  
High Frequency ◽  
Deep Neural Networks ◽  
Detection Method ◽  
Significant Activity ◽  
Detection Model ◽  
Domain Specific ◽  
Conventional Models ◽  
F Measure ◽  
Constrained Devices

Object detection is a significant activity in computer vision, and various approaches have been proposed to detect varied objects using deep neural networks (DNNs). However, because DNNs are computation-intensive, it is difficult to apply them to resource-constrained devices. Here, we propose an on-device object detection method using domain-specific models. In the proposed method, we define object of interest (OOI) groups that contain objects with a high frequency of appearance in specific domains. Compared with the existing DNN model, the layers of the domain-specific models are shallower and narrower, reducing the number of trainable parameters; thus, speeding up the object detection. To ensure a lightweight network design, we combine various network structures to obtain the best-performing lightweight detection model. The experimental results reveal that the size of the proposed lightweight model is 21.7 MB, which is 91.35% and 36.98% smaller than those of YOLOv3-SPP and Tiny-YOLO, respectively. The f-measure achieved on the MS COCO 2017 dataset were 18.3%, 11.9% and 20.3% higher than those of YOLOv3-SPP, Tiny-YOLO and YOLO-Nano, respectively. The results demonstrated that the lightweight model achieved higher efficiency and better performance on non-GPU devices, such as mobile devices and embedded boards, than conventional models.

Scientific Problem Solving in a Virtual Laboratory: A Comparison Between Individuals and Pairs

2008 ◽  
Vol 67 (2) ◽  
pp. 71-83 ◽  
Author(s):  
Yolanda A. Métrailler ◽  
Ester Reijnen ◽  
Cornelia Kneser ◽  
Klaus Opwis
Keyword(s):  
Visual Search ◽  
Problem Solving ◽  
Process Data ◽  
Scientific Problem ◽  
Domain Specific ◽  
Scientific Problem Solving ◽  
Before And After ◽  
Verbal Data ◽  
Psychological Knowledge ◽  
Problem Solving Task

This study compared individuals with pairs in a scientific problem-solving task. Participants interacted with a virtual psychological laboratory called Virtue to reason about a visual search theory. To this end, they created hypotheses, designed experiments, and analyzed and interpreted the results of their experiments in order to discover which of five possible factors affected the visual search process. Before and after their interaction with Virtue, participants took a test measuring theoretical and methodological knowledge. In addition, process data reflecting participants’ experimental activities and verbal data were collected. The results showed a significant but equal increase in knowledge for both groups. We found differences between individuals and pairs in the evaluation of hypotheses in the process data, and in descriptive and explanatory statements in the verbal data. Interacting with Virtue helped all students improve their domain-specific and domain-general psychological knowledge.

Emotions and health: The impact of emotions, emotions regulation, music, and acculturation on health

2008 ◽  
Vol 16 (3) ◽  
pp. 112-115 ◽  
Author(s):  
Stephan Bongard ◽  
Volker Hodapp ◽  
Sonja Rohrmann
Keyword(s):  
Stress Responses ◽  
Physiological Stress ◽  
Anger Expression ◽  
Domain Specific ◽  
Cardiovascular Stress ◽  
Expression Behavior ◽  
Cardio Vascular ◽  
And Coping ◽  
Relationship Of ◽  
The Impact

Abstract. Our unit investigates the relationship of emotional processes (experience, expression, and coping), their physiological correlates and possible health outcomes. We study domain specific anger expression behavior and associated cardio-vascular loads and found e.g. that particularly an open anger expression at work is associated with greater blood pressure. Furthermore, we demonstrated that women may be predisposed for the development of certain mental disorders because of their higher disgust sensitivity. We also pointed out that the suppression of negative emotions leads to increased physiological stress responses which results in a higher risk for cardiovascular diseases. We could show that relaxation as well as music activity like singing in a choir causes increases in the local immune parameter immunoglobuline A. Finally, we are investigating connections between migrants’ strategy of acculturation and health and found e.g. elevated cardiovascular stress responses in migrants when they where highly adapted to the German culture.

Measurement Invariance of Divergent Thinking Across Gender, Age, and School Forms

2009 ◽  
Vol 25 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Jörg-Tobias Kuhn ◽  
Heinz Holling
Keyword(s):  
Measurement Invariance ◽  
Divergent Thinking ◽  
Factor Model ◽  
Age Groups ◽  
Thinking Skills ◽  
Specific Model ◽  
Confirmatory Factor Analyses ◽  
Domain Specific ◽  
Gender And Age ◽  
Confirmatory Factor

The present study explores the factorial structure and the degree of measurement invariance of 12 divergent thinking tests. In a large sample of German students (N = 1328), a three-factor model representing verbal, figural, and numerical divergent thinking was supported. Multigroup confirmatory factor analyses revealed that partial strong measurement invariance was tenable across gender and age groups as well as school forms. Latent mean comparisons resulted in significantly higher divergent thinking skills for females and students in schools with higher mean IQ. Older students exhibited higher latent means on the verbal and figural factor, but not on the numerical factor. These results suggest that a domain-specific model of divergent thinking may be assumed, although further research is needed to elucidate the sources that negatively affect measurement invariance.

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