Results 51 to 60 of about 12,214 (239)

DDVC-YOLOv5: An Improved YOLOv5 Model for Road Defect Detection

IEEE Access
Road defect detection is crucial for enhancing traffic safety, optimizing urban management efficiency, and promoting sustainable urban development. Traditional manual detection methods are inefficient and costly, and most deep learning-based road defect detection models lack superior feature extraction capabilities in complex environments.
Shihao Zhong, Chunlin Chen, Wensheng Luo, Siyuan Chen   +3 more
openaire   +2 more sources

YOLOv5s-CA: A Modified YOLOv5s Network with Coordinate Attention for Underwater Target Detection

Sensors, 2023
Underwater target detection techniques have been extensively applied to underwater vehicles for marine surveillance, aquaculture, and rescue applications. However, due to complex underwater environments and insufficient training samples, the existing underwater target recognition algorithm accuracy is still unsatisfactory.
Ge Wen, Shaobao Li, Fucai Liu, Xiaoyuan Luo, Meng Joo Er, Mufti Mahmud, Tao Wu   +6 more
openaire   +3 more sources

ultralytics/yolov5: v4.0 - nn.SiLU() activations, Weights & Biases logging, PyTorch Hub integration

, 2021
This release implements two architecture changes to YOLOv5, as well as various bug fixes and performance improvements. Breaking Changes nn.SiLU() activations replace nn.LeakyReLU(0.1) and nn.Hardswish() activations used in previous versions.
Marc, Jake Poznanski, Jirka Borovec, Durgesh, Hu Ye, Junghoon Kim, lorenzomammana, AlexWang1900, ChristopherSTAN, Jacobsolawetz, Adam Hogan, Liu Changyu, Doug, Laurentiu Diaconu, Khiem Doan, yxNONG, wanghaoyang0106, Jiacong Fang, Glenn Jocher, ml5ah, Adrien Colmagro, Francisco Ingham, Alex Stoken, Lijun Yu 于力军, NanoCode012, Laughing, Frederik, tkianai, Guilhen, Ayush Chaurasia   +29 more
core   +1 more source

Swin-YOLOv5: Research and Application of Fire and Smoke Detection Algorithm Based on YOLOv5

Computational Intelligence and Neuroscience, 2022
Accurate monitoring of fire and smoke plays an irreplaceable role in preventing fires and safeguarding the safety of citizens' lives and property. The network structure of YOLOv5 is simple, but using convolution to extract features will lead to some problems such as limited receptive field, poor feature extraction ability, and insufficient feature ...
Shangjie Ge Zhang, Fengxi Zhang, Yuyang Ding, Yu Li   +3 more
openaire   +2 more sources

Deployment of Kidney Tumor Disease Object Detection Using CT-Scan with YOLOv5

Journal of Applied Informatics and Computing
Image processing plays a crucial role in identifying kidney tumors through CT-Scan images. Object detection technology, particularly YOLO, stands out for its speed and accuracy in facilitating more detailed analysis. Using Flask as a web framework offers
Hastyantoko Dwiki Kahingide, Abu Salam
doaj   +1 more source

Counting objects in an industrial environments using YOLOv5 and YOLOv7

, 2023
reservedSia Yolov5 che Yolov7 sono modelli all’avanguardia per il rilevamento e il conteggio degli oggetti. Hanno assistito gli ingegneri del machine learning nell’addestramento efficace dei loro set di dati con risultati eccezionali.
TARIQ, RAAZIA
core  

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

HDS-YOLOv5: An improved safety harness hook detection algorithm based on YOLOv5s

Mathematical Biosciences and Engineering, 2023
<abstract> <p>Improperly using safety harness hooks is a major factor of safety hazards during power maintenance operation. The machine vision-based traditional detection methods have low accuracy and limited real-time effectiveness. In order to quickly discern the status of hooks and reduce safety incidents in the complicated operation ...
Mingju Chen, Zhongxiao Lan, Zhengxu Duan, Sihang Yi, Qin Su   +4 more
openaire   +3 more sources

Experimental Demonstration of Temporally Aware Fault‐Tolerant Sensor Fusion Using Memristive Associative Learning

Advanced Electronic Materials, EarlyView.
In dynamic driving scenarios, the proposed approach ensures only temporally aligned sensor inputs to make driving decisions, preventing false activations. By enabling selective hardware‐level learning, it achieves fast, reliable responses under noisy conditions.
Kapil Bhardwaj, Ashfak Nehal, Dmitrii Semenov, Roman Sotner, Sayani Majumdar   +4 more
wiley   +1 more source

Implementasi Algoritma Convolutional Neural Network untuk Pendeteksi Objek dalam Rumah pada Mata Rabun

Jurnal Saintekom, 2023
The increased use of laptops and smartphones during the COVID-19 pandemic has led to an increase in the number of people suffering from nearsightedness. Convolutional Neural Network (CNN) is a class of deep learning that is capable of recognizing images ...
Pramadika Egamo, Arief Hermawan
doaj   +1 more source