Performance Evaluation of Fine-tuned Faster R-CNN on specific MS COCO Objects

Devnani, Garima; Jaiswal, Ayush; John, Roshni; Chaurasia, Rajat; Tirpude, Neha

doi:10.11591/ijece.v9i4.pp2548-2555

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…There were 14 studies based on tables that used public datasets. Typically, public datasets may be found in the MS COCO dataset, which contains about 330 thousand photos and 80 object categories [13], [56], ImageNet is a database of over a million photos and one thousand different kinds of objects [43] and the Kaggle dataset [57], which has been collected manually by the researcher and published on it. Besides, Roboflow was also one of the public datasets that new researchers or other users could access.…”

Section: Dataset Preparationmentioning

confidence: 99%

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

et al. 2023

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Deep learning has shown much promise in target identification in recent years, and it's becoming more popular in agriculture, where fig fruit detection and counting have become important. In this study, a systematic literature review (SLR) is utilised to evaluate a deep learning algorithm for detecting and counting fig fruits. The SLR is based on the widely used 'Reporting Standards for Systematic Evidence Synthetics' (ROSES) review process. The study starts by formulating the research questions, and the proposed SLR approach is critically discussed until the data abstraction and analysis process is completed. Following that, 33 relevant research involving the agriculture sector, fruit, were selected from many studies. IEEE, Scopus, and Web of Sciences are three databases to investigate. Due to the lack of fig fruit research, fruit and vegetable studies have been included because they use similar methods and processes. The SLR found that various deep learning algorithms can count fig fruit in the field. Furthermore, as most approaches obtained acceptable results, deep learning's performance is acceptable in F1-score and average precision (AP), higher than 80%. Moreover, improvements can be produced by enhancing the existing deep learning model with the personal dataset.

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Section: Dataset Preparationmentioning

confidence: 99%

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

et al. 2023

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“…Faster R-CNN has 9 anchors consisting of 3 scales and 3 ratios that make this method can detect objects more accurately [11]- [13]. When we use R-CNN, the bounding boxes (BBs) are generated [14].…”

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confidence: 99%

Comparison of two deep learning methods for detecting fire hotspots

Lestari

Kosasih

2022

IJECE

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Every high-rise building must meet construction requirements, i.e. it must have good safety to prevent unexpected events such as fire incident. To avoid the occurrence of a bigger fire, surveillance using closed circuit television (CCTV) videos is necessary. However, it is impossible for security forces to monitor for a full day. One of the methods that can be used to help security forces is deep learning method. In this study, we use two deep learning methods to detect fire hotspots, i.e. you only look once (YOLO) method and faster region-based convolutional neural network (faster R-CNN) method. The first stage, we collected 100 image data (70 training data and 30 test data). The next stage is model training which aims to make the model can recognize fire. Later, we calculate precision, recall, accuracy, and F1 score to measure performance of model. If the F1 score is close to 1, then the balance is optimal. In our experiment results, we found that YOLO has a precision is 100%, recall is 54.54%, accuracy is 66.67%, and F1 score is 0.70583667. While faster R-CNN has a precision is 87.5%, recall is 95.45%, accuracy is 86.67%, and F1 score is 0.913022.

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“…For applications that involve the use of robots in integrated environments as assistants, it is necessary to identify the object of interest and grasp it, for which CNN has already shown their versatility [19,20]. This work presents an advance in the use of DL for the recognition and grip of objects in multi-objective environments oriented to assistive robots using recent techniques of variation of conventional CNN architectures such as fast-RCNN and CNN regression [21].…”

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confidence: 99%

Object gripping algorithm for robotic assistance by means of deep learning

Moreno

Rubiano

Ramírez

2020

IJECE

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This paper exposes the use of recent deep learning techniques in the state of the art, little addressed in robotic applications, where a new algorithm based on Faster R-CNN and CNN regression is exposed. The machine vision systems implemented, tend to require multiple stages to locate an object and allow a robot to take it, increasing the noise in the system and the processing times. The convolutional networks based on regions allow one to solve this problem, it is used for it two convolutional architectures, one for classification and location of three types of objects and one to determine the grip angle for a robotic gripper. Under the establish virtual environment, the grip algorithm works up to 5 frames per second with a 100% object classification, and with the implementation of the Faster R-CNN, it allows obtain 100% accuracy in the classifications of the test database, and over a 97% of average precision locating the generated boxes in each element, gripping successfully the objects.

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Performance Evaluation of Fine-tuned Faster R-CNN on specific MS COCO Objects

Cited by 4 publications

References 7 publications

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

Comparison of two deep learning methods for detecting fire hotspots

Object gripping algorithm for robotic assistance by means of deep learning

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