Detection and Localization of Multiple Objects Using VGGNet and Single Shot Detection

Sathish, Kuppani; Ramasubbareddy, Somula; Govinda, K.

doi:10.1007/978-981-15-0135-7_40

Cited by 17 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of the model gets better after unfreezing the weights because the whole model will be trained on the particular dataset provided by us. The parameters in the model get updated by back propagation which means after calculating the loss between the actual and the predicted output the parameters gets updated with respect to the loss value [26][27][28][29][30].…”

Section: Proposed Methodsmentioning

confidence: 99%

Fruit Recognition Using Deep Learning

Reddy

Ramasubbareddy

Saidulu

et al. 2021

Innovations in Computer Science and Engineering

Self Cite

View full text Add to dashboard Cite

This paper discusses on the fruits classification for which the data is collected from the dataset called Fruits_360. Using this data, training of a neural network which will identify the fruit. Using the deep learning and image processing concepts form a neural networking system. The proposed work uses convolution neural networks in building the model and also used ResNet to get the results of image classification from deep learning concept. To meet the resource requirement for the proposed work, it uses Google cloud vision API which gives us the required GPU to proceed with the process of analyzing the data from the image and also discussed the in depth of how image classification is done using deep learning concepts. Here, in building a deep learning model, which classifies the given image into any of these nine categories: Apple, Avocado, Banana, Cherry, Cocos, Kiwi, Mango, Orange, Lemon. This model can also be implemented into mobile version.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Fruit Recognition Using Deep Learning

Reddy

Ramasubbareddy

Saidulu

et al. 2021

Innovations in Computer Science and Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, intelligent computing methods develop rapidly, and a series of excellent performance of deep network models, such as: Alex-Net, VGG-Net, SuperGraph, etc [10][11][12], have sprung up. These methods overcome the complex framework, detection performance problems, and the requirement of expert knowledge, and performing accurate classification tasks benefitting from high-efficiency-feature learning and high-nonlinear models [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

An imbalance aware lithography hotspot detection method based on HDAM and pre-trained GoogLeNet

Zhang¹,

Liu²,

Liu³

et al. 2021

Meas. Sci. Technol.

View full text Add to dashboard Cite

Due to the continuous shrinkage of transistor size and the ever-increasing complexity of integrated circuit design layout, great challenges arise in optical lithography-any defect on the mask will be transferred to the silicon wafer, which may lead to severe defects such as open circuit and short circuit. These defects on masks are called hotspots. Before transferring the circuit layout on the mask to the silicon wafer, the entire mask must be inspected to accurately find out the hotspots before optical lithography. Although traditional lithography hotspot detection approaches, such as pattern matching and machine learning, have gained satisfactory results the performance of the model degrades when encountering problems such as complex layout and data imbalance. In this paper, a hotspot detection method based on hybrid data enhancement, data compression and pre-trained GoogLeNet is proposed to solve the aforementioned problems. Our study shows that the average recall rate can be up to 98.3%. Meanwhile, the false alarm is reduced and the F1-score is 63.5%. Experimental results show that the proposed method achieves better performance on the ICCAD 2012 contest benchmark compared to hotspot detection methods based on deep or representative machine learning.

show abstract

Convolutional neural network and its pretrained models for image classification and object detection: A survey

Jena

Nayak

Saxena

2021

Concurrency and Computation

View full text Add to dashboard Cite

At present, in the age of computers and automation of services, deep learning (DL) technology, mainly the subset of machine learning (ML) and artificial intelligence (AI), is expressively used in innumerable domains of computer vision such as data analysis, image recognition, classification, natural language processing, and many more. It has become the foremost choice of researchers as of its effectiveness in producing decent results. This paper presents detailed and analytical literature starting from the very elementary level to the recent trends of this trending technology while focusing on the most used DL model, that is, convolutional neural network and its pretrained models for image classification and object detection. It also reviews diverse existing current literature based on this. Further, a brief introduction of AI, ML, and DL has also been presented, making the foundation for the readers. As pretrained models continuously give an upper edge to DL over ML and other technologies, 23 most popular pretrained models with their architectural diagrams have also been presented. This paper aims to summarize and analyze all the concepts used to formulate DL and its models. Also, we have emphasized more on the GoogleNet models and the entire Inception modules in detail. Finally, the fascinating applications and discussion on integral components of DL have been presented. This paper will definitely draw the attention of the students and researchers working in the area of DL and its models.

show abstract

Detection and Localization of Multiple Objects Using VGGNet and Single Shot Detection

Cited by 17 publications

References 3 publications

Fruit Recognition Using Deep Learning

Fruit Recognition Using Deep Learning

An imbalance aware lithography hotspot detection method based on HDAM and pre-trained GoogLeNet

Convolutional neural network and its pretrained models for image classification and object detection: A survey

Contact Info

Product

Resources

About