Faster R-CNN with Inception V2 for Fingertip Detection in Homogenous Background Image

Alamsyah, Derry; Fachrurrozi, Muhammad

doi:10.1088/1742-6596/1196/1/012017

Cited by 30 publications

(17 citation statements)

References 3 publications

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“…There are a variety set of state-of-the-art CNN models that can be implemented as base-models for transfer learning such as VGG16 (Simonyan, and Zisserman, 2014), VGG19 (Wen et al, 2019), MobileNet V2 (Sandler et al, 2018), Xception (Chollet, 2017), Inception V2 (Alamsyah and Fachrurrozi, 2019), Inception-Resnet-V2 (Szegedy et al, 2017) and more.…”

Section: The Proposed Hybrid DL Approach For Classification Step 221 the Transfer Learning Techniquementioning

confidence: 99%

Towards a Reliable and Sensitive Deep Learning Based Approach for Multi-Ship Detection in SAR Imagery

Elshafey

2021

IJG

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Synthetic Aperture Radar (SAR) images show promising results in monitoring maritime activities. Recently, Deep learning-based object detection techniques have impressive results in most detection applications but unfortunately there are challenging problems such as difficulty of detecting multiple ships, especially inshore ones. In this paper, a three-step ship detection process is described and a reliable and sensitive hybrid deep learning model is proposed as an efficient classifier in the middle step. The proposed model combines the finetuned Inception-Resnet-V2 model and the Long Short Term Memory model in two different approaches: parallel approach and cascaded approach. In experiments, the region proposal algorithm and the Non-Maxima suppression algorithm are applied in the first and last step in the three-step detection process. The comparative results show that the proposed approach in cascaded form outperforms the competitive recent state-of-the-art approaches by enhancement up to 16.3%, 16.5%, and 18.9% in terms of recall, precision and mean average precision, respectively. Moreover, the proposed approach shows high relative sensitivity for challenged cases of both inshore and offshore scenes by enhancement ratios up to 81.88% and 24.58%, respectively in recall perspective.

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Section: The Proposed Hybrid DL Approach For Classification Step 221 the Transfer Learning Techniquementioning

confidence: 99%

Towards a Reliable and Sensitive Deep Learning Based Approach for Multi-Ship Detection in SAR Imagery

Elshafey

2021

IJG

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“…This module causes the convolution network to be broader rather than deeper. There are three types of modules in Inception V2 [18].…”

Section: A Faster Regions With Convolutional Neural Network (R-cnn) Inception V2mentioning

confidence: 99%

A Recognition Method for Cassava Phytoplasma Disease (CPD) Real-Time Detection based on Transfer Learning Neural Networks

Plata¹,

Panganiban²,

Alado³

et al. 2021

IJACSA

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Object detection technology aims to detect the target objects with the theories and methods of image processing and pattern recognition, determine the semantic categories of these objects, and mark the specific position of the target object in the image. This study generally aims to establish a recognition method for Cassava Phytoplasma Disease (CPD) real-time detection based on transfer learning neural networks. Several methods and procedures were conducted, such as the testing of two methods in transmitting long-distance high definition (HD) video capture; establishment of a compact setup for a long-range wireless video transmission system; the development, testing of the real-time CPD detection and quantification monitoring system, providing the comparative performance analysis of the three models used. We have successfully custom-trained three artificial neural networks using transfer learning: Faster Regions with Convolutional Neural Networks (R-CNN) Inception v2, Single Shot Detector (SSD) Mobilenet v2, and You Only Look Once (YOLO) v4. These deep learning models can detect and recognize CPD in actual environment settings. Overall, the developed real-time CPD detection and quantification monitoring system was successfully integrated into the wireless video receiver and seamlessly visualized all the incoming data using the three different CNN models. If the consideration is the image processing speed, YOLOv4 is better compared to other models. But, if accuracy is the priority, Faster R-CNN inception v2 performs better. However, since CPD detection is the main purpose of this study, the Faster R-CNN model is recommended for adoption to detect CPD in a real-time environment.

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“…For training RPNs, a binary class label (of being an object or not) has been assigned to each anchor. Equation (2) represented a loss function for an image following the multi-task loss in Fast R-CNN ( Alamsyah & Fachrurrozi, 2019 ; Ren et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework

Bari

Islam

Rashid

et al. 2021

PeerJ Computer Science

177

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The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms’ edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.

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Faster R-CNN with Inception V2 for Fingertip Detection in Homogenous Background Image

Cited by 30 publications

References 3 publications

Towards a Reliable and Sensitive Deep Learning Based Approach for Multi-Ship Detection in SAR Imagery

Towards a Reliable and Sensitive Deep Learning Based Approach for Multi-Ship Detection in SAR Imagery

A Recognition Method for Cassava Phytoplasma Disease (CPD) Real-Time Detection based on Transfer Learning Neural Networks

A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework

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