Performance Analysis and Comparison of Faster R-CNN, Mask R-CNN and ResNet50 for the Detection and Counting of Vehicles

Tahir, Hassam; Khan, Muhammad Shahbaz; Tariq, Muhammad

doi:10.1109/icccis51004.2021.9397079

Cited by 23 publications

(7 citation statements)

References 14 publications

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“…With the evolution of artificial intelligence (AI) and deep learning (DL), solutions based on specially designed neural networks have become more accurate and reliable [23]. Neural networks, particularly CNNs, have been used in various applications involving disease diagnosis [24][25][26].…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases Classification

Altabaji,

Umair,

Tan

et al. 2024

IEEE Access

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Early detection of plant diseases is essential for effective crop disease management to prevent yield loss. In this study, we developed a methodology for classifying diseases in rice leaves using four deep learning models and a dataset with 2658 images of healthy and diseased rice leaves. Four models, namely LeafNet, Modified LeafNet, MobileNetV2, and Xception, were compared. The Modified LeafNet model involved updates to LeafNet's architectural parameters, whereas transfer learning techniques were applied to the MobileNetV2 and Xception pretrained models. The optimal hyperparameters for training were determined by considering several factors such as batch size, data augmentation, learning rate, and optimizers. The Modified LeafNet model achieved the highest accuracies of 97.44% and 87.76% for the validation and testing datasets, respectively. In comparison, LeafNet obtained 88.92% and 71.84%, Xception obtained 88.64% and 71.95%, and MobileNetV2 obtained 82.10% and 67.68% for the validation and test accuracies on the same datasets, respectively. This study contributes to the development of automated disease classification systems for rice leaves, thereby leading to increased agricultural productivity and sustainability.

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Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases Classification

Altabaji,

Umair,

Tan

et al. 2024

IEEE Access

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“…The residual module is innovatively proposed in ResNet50, which effectively solves the problem of deep network degradation with deepening layers in convolutional neural networks 3 . It has been widely used in recent years, see 1,[4][5][6]8,[10][11][12][13] . Therefore, ResNet50 is chosen as the basic network in this paper.…”

Section: Model Building 21 Dual Channel Resnet50 Networkmentioning

confidence: 99%

Satellite pose estimation network based on dual-channel ResNet50

Wang

Zhang

et al. 2023

Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022)

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In the satellite pose estimation problem, the deep learning method is used to train the network. The satellite pose needs to estimate the rotation (R) and translation (T), which are difficult to be well estimated simultaneously due to the internal coupling interaction. To solve the above problems, a dual-channel satellite pose estimation network based on ResNet50 is proposed to decouple the rotation and translation of satellite, effectively avoid the interaction, and estimate the translation and rotation of satellite respectively through the constructed network, which improves the recognition effect of satellite attitude. Through experimental verification, the network model constructed in this paper has better effect on the estimation of rotation and translation compared with other methods.

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“…Deep learning for meme classification author Manoj Balaji J, Chinmaya HS in the year of 2021 uses the dataset 'Tamil Troll Memes' Suryawanshi et al (2020a) and algorithm for classifying memes is Resnet-50. The network can grasp features from different portions and levels of the image because of the architecture's 50 layers [5]. The photos are downsized to 64x64 pixels, and all three-color channels are input into the algorithm, which is then given to the neural network's subsequent layers to produce the final output.…”

Section: Literature Surveymentioning

confidence: 99%

Troll Meme Classification for Tamil Memes Using Convolutional Neural Network

Nithya¹,

Sathyapriya²,

Sulochana³

et al. 2023

ACE

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Memes have become a new type of internet communication. It has the ability to instantly disseminate anger, offensiveness, and violence. Because of its regional meaning, classifying memes is difficult.. This work presents here a computational model of classifying Tamil memes using convolutional neural networks. Convolutional neural networks have the potential to learn, adapt, and rearrange themselves. As a result, it can extract features automatically applying prior knowledge of existing categories, avoiding the time-consuming feature extraction process used in older methods in images. The basic layer of MobileNet is made up of depth-wise separable filters, also referred to as depth-wise separable convolution. The network structure is another feature that boosts performance. It utilizes very less than computation power while applying transfer learning. This network has reduced parameters and computation cost. Skip connections, or shortcuts, are used by residual neural networks to jump past some layers. Residual connections allow parameter gradients to travel more easily from the output layer to the network's prior layers, allowing for the training of deeper networks. Higher accuracies on more demanding tasks may arise from the greater network depth. AlexNet is a leading architecture for any image identification task, and it could have a lot of applications in the artificial intelligence field of computer vision. In the future, AlexNet may be used for image classification jobs more than CNNs. This work aims to classify Tamil memes using Mobilenet, Resnet and hyper parameter turned AlexNet.

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Performance Analysis and Comparison of Faster R-CNN, Mask R-CNN and ResNet50 for the Detection and Counting of Vehicles

Cited by 23 publications

References 14 publications

Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases Classification

Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases Classification

Satellite pose estimation network based on dual-channel ResNet50

Troll Meme Classification for Tamil Memes Using Convolutional Neural Network

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