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 …

Normalization techniques in training dnns: Methodology, analysis and application

L Huang, J Qin, Y Zhou, F Zhu, L Liu… - IEEE transactions on …, 2023 - ieeexplore.ieee.org
Normalization techniques are essential for accelerating the training and improving the
generalization of deep neural networks (DNNs), and have successfully been used in various …

Yolov4: Optimal speed and accuracy of object detection

A Bochkovskiy, CY Wang, HYM Liao - arxiv preprint arxiv:2004.10934, 2020 - arxiv.org
There are a huge number of features which are said to improve Convolutional Neural
Network (CNN) accuracy. Practical testing of combinations of such features on large …

A fast accurate fine-grain object detection model based on YOLOv4 deep neural network

AM Roy, R Bose, J Bhaduri - Neural Computing and Applications, 2022 - Springer
Early identification and prevention of various plant diseases is a key feature of precision
agriculture technology. This paper presents a high-performance real-time fine-grain object …

WilDect-YOLO: An efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection

AM Roy, J Bhaduri, T Kumar, K Raj - Ecological Informatics, 2023 - Elsevier
Objective. With climatic instability, various ecological disturbances, and human actions
threaten the existence of various endangered wildlife species. Therefore, an up-to-date …

Real-time growth stage detection model for high degree of occultation using DenseNet-fused YOLOv4

AM Roy, J Bhaduri - Computers and Electronics in Agriculture, 2022 - Elsevier
Real-time detection of agricultural growth stages is one of the key steps of estimating yield
and intelligent spraying in commercial orchards. However, due to considerable degree of …

Deepsocial: Social distancing monitoring and infection risk assessment in covid-19 pandemic

M Rezaei, M Azarmi - Applied Sciences, 2020 - mdpi.com
Social distancing is a recommended solution by the World Health Organisation (WHO) to
minimise the spread of COVID-19 in public places. The majority of governments and …

In-depth review of yolov1 to yolov10 variants for enhanced photovoltaic defect detection

M Hussain, R Khanam - Solar, 2024 - mdpi.com
This review presents an investigation into the incremental advancements in the YOLO (You
Only Look Once) architecture and its derivatives, with a specific focus on their pivotal …

Real-time railroad track components inspection based on the improved YOLOv4 framework

F Guo, Y Qian, Y Shi - Automation in construction, 2021 - Elsevier
Abstract According to the Federal Railroad Administration (FRA) database, track component
failure is one of the major factors causing train accidents. To improve railroad safety and …

A comprehensive review of convolutional neural networks for defect detection in industrial applications

R Khanam, M Hussain, R Hill, P Allen - IEEE Access, 2024 - ieeexplore.ieee.org
Quality inspection and defect detection remain critical challenges across diverse industrial
applications. Driven by advancements in Deep Learning, Convolutional Neural Networks …