TensorFlow with user friendly Graphical Framework for object detection API

Yoon, Heemoon; Lee, Sang‐Hee; Park, Soo‐Jin

doi:10.48550/arxiv.2006.06385

Cited by 2 publications

(2 citation statements)

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“…In order to address the class number imbalance, we utilized the linear inverse class frequency to regulate the weighted cross-entropy losses, as suggested in previous studies [ 34 , 35 ]. For the implementation of the sperm acrosome object detection, we employed the TensorFlow object detection package [ 36 ] and its extension. The Faster R-CNN model was pretrained on the COCO dataset [ 37 ] and then fine-tuned using the training dataset to detect AR/Non-AR.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Precision Analysis for Acrosome Reaction by Modification of Plasma Membrane in Boar Sperm

Park

Yoon

Kang

et al. 2023

Animals

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The analysis of AR is widely used to detect loss of acrosome in sperm, but the subjective decisions of experts affect the accuracy of the examination. Therefore, we develop an ARCS for objectivity and consistency of analysis using convolutional neural networks (CNNs) trained with various magnification images. Our models were trained on 215 microscopic images at 400× and 438 images at 1000× magnification using the ResNet 50 and Inception–ResNet v2 architectures. These models distinctly recognized micro-changes in the PM of AR sperms. Moreover, the Inception–ResNet v2-based ARCS achieved a mean average precision of over 97%. Our system’s calculation of the AR ratio on the test dataset produced results similar to the work of the three experts and could do so more quickly. Our model streamlines sperm detection and AR status determination using a CNN-based approach, replacing laborious tasks and expert assessments. The ARCS offers consistent AR sperm detection, reduced human error, and decreased working time. In conclusion, our study suggests the feasibility and benefits of using a sperm diagnosis artificial intelligence assistance system in routine practice scenarios.

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Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Precision Analysis for Acrosome Reaction by Modification of Plasma Membrane in Boar Sperm

Park

Yoon

Kang

et al. 2023

Animals

Self Cite

View full text Add to dashboard Cite

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“…Additionally, efforts have been made to enhance the neural network architecture by increasing its depth and complexity [5,[20][21][22][23][24]. Optimization of training hyperparameters has also been explored [25][26][27][28][29][30][31][32]. However, the literature lacks a clear consensus on the ideal size of a training dataset [17,[33][34][35][36][37][38], indicating a research gap concerning the optimal dataset scale for training object detectors.…”

Section: Literature Reviewmentioning

confidence: 99%

A Framework to Estimate the Key Point Within an Object Based on a Deep Learning Object Detection

Kurdthongmee¹,

Suwannarat²,

Wattanapanich³

2023

HighTech. Innov. J.

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Automatic identification of key points within objects is crucial in various application domains. This paper presents a novel framework for accurately estimating the key point within an object by leveraging deep neural network-based object detection. The proposed framework is built upon a training dataset annotated with four non-overlapping bounding boxes, one of which shares a coordinate with the key point. These bounding boxes collectively cover the entire object, enabling automatic annotation if region annotations around the key point exist. The trained object detector is then utilized to generate detection results, which are subsequently post-processed to estimate the key point. To validate the effectiveness of the framework, experiments were conducted using two distinct datasets: cross-sectional images of a parawood log and pupil images. The experimental results demonstrate that our proposed framework surpasses previously proposed approaches in terms of precision, recall, F1-score, and other domain-specific metrics. The improvement in performance can be attributed to the unique annotation strategy and the fusion of object detection and key point estimation within a unified deep learning framework. The contribution of this study lies in introducing a novel framework for closely estimating key points within objects based on deep neural network-based object detection. By leveraging annotated training data and post-processing techniques, our approach achieves superior performance compared to existing methods. This work fills a critical gap in the field by integrating object detection and key point estimation, which has received limited attention in previous research. Our framework provides valuable insights and advancements in key point estimation techniques, offering potential applications in precise object analysis and understanding. Doi: 10.28991/HIJ-2023-04-01-08 Full Text: PDF

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TensorFlow with user friendly Graphical Framework for object detection API

Cited by 2 publications

References 0 publications

Deep Learning-Based Precision Analysis for Acrosome Reaction by Modification of Plasma Membrane in Boar Sperm

Deep Learning-Based Precision Analysis for Acrosome Reaction by Modification of Plasma Membrane in Boar Sperm

A Framework to Estimate the Key Point Within an Object Based on a Deep Learning Object Detection

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