Object detection using YOLO: Challenges, architectural successors, datasets and applications
Object detection is one of the predominant and challenging problems in computer vision.
Over the decade, with the expeditious evolution of deep learning, researchers have …
Over the decade, with the expeditious evolution of deep learning, researchers have …
YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection
M Hussain - Machines, 2023 - mdpi.com
Since its inception in 2015, the YOLO (You Only Look Once) variant of object detectors has
rapidly grown, with the latest release of YOLO-v8 in January 2023. YOLO variants are …
rapidly grown, with the latest release of YOLO-v8 in January 2023. YOLO variants are …
Deep residual learning for image recognition: A survey
Deep Residual Networks have recently been shown to significantly improve the
performance of neural networks trained on ImageNet, with results beating all previous …
performance of neural networks trained on ImageNet, with results beating all previous …
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Convolutional neural networks: A survey
M Krichen - Computers, 2023 - mdpi.com
Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing
industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of …
industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of …
A review of safe reinforcement learning: Methods, theory and applications
Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …
making tasks. However, safety concerns are raised during deploying RL in real-world …
Artificial intelligence-based robust hybrid algorithm design and implementation for real-time detection of plant diseases in agricultural environments
Simple Summary Plant disease, defined as an abnormal condition that disrupts the normal
growth of the plant, is one of the main causes of economic losses in the agricultural industry …
growth of the plant, is one of the main causes of economic losses in the agricultural industry …
[HTML][HTML] Review of image classification algorithms based on convolutional neural networks
L Chen, S Li, Q Bai, J Yang, S Jiang, Y Miao - Remote Sensing, 2021 - mdpi.com
Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …
emergence of deep learning has promoted the development of this field. Convolutional …
Forecasting monthly gas field production based on the CNN-LSTM model
W Zha, Y Liu, Y Wan, R Luo, D Li, S Yang, Y Xu - Energy, 2022 - Elsevier
Accurate prediction of gas field production is an important task for reservoir engineers, which
is challenging due to many unknown reservoir parameters. Aiming to have a low-cost …
is challenging due to many unknown reservoir parameters. Aiming to have a low-cost …
A survey on deep learning and its applications
Deep learning, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …