COCO (Creating Common Object in Context) Dataset for Chemistry Apparatus

Rostianingsih, Silvia; Setiawan, Alexander; Halim, Christopher

doi:10.1016/j.procs.2020.04.264

Cited by 25 publications

(18 citation statements)

References 7 publications

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“… was trained using cropped labelled training set and was trained using cropped unlabelled images and their corresponding pseudo labels generated by . We used transfer learning, where the models were initially pre-trained on Microsoft’s COCO dataset 31 . All models were trained using a batch size of 8 with a learning rate of for maximum epochs of 100.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Dental caries detection using a semi-supervised learning approach

Qayyum

Tahir

Butt

et al. 2023

Sci Rep

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Early diagnosis of dental caries progression can prevent invasive treatment and enable preventive treatment. In this regard, dental radiography is a widely used tool to capture dental visuals that are used for the detection and diagnosis of caries. Different deep learning (DL) techniques have been used to automatically analyse dental images for caries detection. However, most of these techniques require large-scale annotated data to train DL models. On the other hand, in clinical settings, such medical images are scarcely available and annotations are costly and time-consuming. To this end, we present an efficient self-training-based method for caries detection and segmentation that leverages a small set of labelled images for training the teacher model and a large collection of unlabelled images for training the student model. We also propose to use centroid cropped images of the caries region and different augmentation techniques for the training of self-supervised models that provide computational and performance gains as compared to fully supervised learning and standard self-supervised learning methods. We present a fully labelled dental radiographic dataset of 141 images that are used for the evaluation of baseline and proposed models. Our proposed self-supervised learning strategy has provided performance improvement of approximately 6% and 3% in terms of average pixel accuracy and mean intersection over union, respectively as compared to standard self-supervised learning. Data and code will be made available to facilitate future research.

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Section: Experiments and Resultsmentioning

confidence: 99%

Dental caries detection using a semi-supervised learning approach

Qayyum

Tahir

Butt

et al. 2023

Sci Rep

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“…After completing the above mentioned steps in Roboflow, the data were exported in COCO JSON format and used in the experiment. COCO is dataset format, and specific JSON structure, used by Microsoft, Google, and Facebook [12]. This format is needed for working with Detectron2.…”

Section: B Preprocessing Of the Imagesmentioning

confidence: 99%

Image-Based Parking Occupancy Detection Using Deep Learning and Faster R-CNN

Scekic

Čakić

Popović

et al. 2022

2022 26th International Conference on Information Technology (IT)

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Smart city is one area with the growing use of Internet of Things and Artificial Intelligence. The concept of smart cities relies on making quality of life better, and solving important problems, such as global warming, public health, energy and resources. Smart parking management is one of the smart city use cases. This paper describes the use of deep learning algorithms to process images of parking lots and determine their current occupancy. The development of prediction models was done using PKLot dataset with 12417 images, Detectron2 software library, and Faster R-CNN algorithm. The resulting models can be integrated into parking space sensors and used for building smart parking solutions, and thus lead to more efficient use of space in urban areas, reduced traffic congestion, as well as reducing parking surfing to minimum.

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“…This paper uses the Penn-Fudan Database for pedestrian detection as well as segmentation (see Figure 3), which is available on the website (https://www.kaggle.com/jiweiliu/ pennfudanpe, accessed on 1 February 2021). It contains 170 images with 345 pedestrian objects, and it is compatible with both COCO [55][56][57] and Pascal VOC format [54]. We used the dataset during our research in COCO format.…”

Section: Datasetmentioning

confidence: 99%

“…Data Availability Statement: To explore the environmental conditions, a pedestrian custom dataset based on Common Object in Context (COCO) was used [55][56][57].…”

Section: Informed Consent Statement: Not Applicablementioning

confidence: 99%

Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions

2021

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Self-driving cars, i.e., fully automated cars, will spread in the upcoming two decades, according to the representatives of automotive industries; owing to technological breakthroughs in the fourth industrial revolution, as the introduction of deep learning has completely changed the concept of automation. There is considerable research being conducted regarding object detection systems, for instance, lane, pedestrian, or signal detection. This paper specifically focuses on pedestrian detection while the car is moving on the road, where speed and environmental conditions affect visibility. To explore the environmental conditions, a pedestrian custom dataset based on Common Object in Context (COCO) is used. The images are manipulated with the inverse gamma correction method, in which pixel values are changed to make a sequence of bright and dark images. The gamma correction method is directly related to luminance intensity. This paper presents a flexible, simple detection system called Mask R-CNN, which works on top of the Faster R-CNN (Region Based Convolutional Neural Network) model. Mask R-CNN uses one extra feature instance segmentation in addition to two available features in the Faster R-CNN, called object recognition. The performance of the Mask R-CNN models is checked by using different Convolutional Neural Network (CNN) models as a backbone. This approach might help future work, especially when dealing with different lighting conditions.

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COCO (Creating Common Object in Context) Dataset for Chemistry Apparatus

Cited by 25 publications

References 7 publications

Dental caries detection using a semi-supervised learning approach

Dental caries detection using a semi-supervised learning approach

Image-Based Parking Occupancy Detection Using Deep Learning and Faster R-CNN

Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions

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