Results 131 to 140 of about 12,214 (239)

Research on multi-objective detection method for incomplete information in coal mine underground

Meitan kexue jishu
Underground target detection technology in coal mines is an indispensable component of constructing a smart mine, providing real-time monitoring and recognition capabilities.
Lin SUN, Sheng CHEN, Xulong YAO, Yanbo ZHANG, Zhigang TAO, Peng LIANG   +5 more
doaj   +1 more source

The recognition of dust emission from open-pit mining roads based on cascaded convolutional networks by deep learning

Meitan kexue jishu
The dust emitted from open-pit mines poses a constant menace to both the health of the workers and the environment. However, the traditional manual decision-making and scheduled watering methods are constrained by delayed dust control and excessive water
Yunzhuo ZHOU, Zijing XU, Lin BI
doaj   +1 more source

The network structure of improved YOLOv5 model.

Workpiece surface defect detection is an indispensable part of intelligent production. The surface information obtained by traditional 2D image detection has some limitations due to the influence of environmental light factors and part complexity ...
Danya Zhang (15309566), Xuesong Huang (18468785), Ligang Qu (18468782), Zeng Chen (6910307)   +3 more
core   +1 more source

Fe-Yolov5: Feature Enhancement Network Based on Yolov5 for Small Object Detection

SSRN Electronic Journal, 2022
Min Wang, Wenzhong Yang, Liejun Wang, Danny Chen, Fuyuan Wei, HaiLaTi KeZiErBieKe, Yuanyuan Liao   +6 more
openaire   +1 more source

STBNA-YOLOv5: An Improved YOLOv5 Network for Weed Detection in Rapeseed Field

Agriculture
Rapeseed is one of the primary oil crops; yet, it faces significant threats from weeds. The ideal method for applying herbicides would be selective variable spraying, but the primary challenge lies in automatically identifying weeds. To address the issues of dense weed identification, frequent occlusion, and varying weed sizes in rapeseed fields, this ...
Tao Tao, Xinhua Wei
openaire   +2 more sources

Real time Optical  Character Recognition  in  steel  bars  using YOLOV5

, 2023
Background.Identifying the quality of the products in the manufacturing industry is a challenging task. Manufacturers use needles to print unique numbers on the products to differentiate between good and bad quality products. However, identi- fying these
Gattupalli, Monica
core  

Pedestrian detection method based on improved YOLOv5

With the development of autonomous vehicles and intelligent transportation, more accurate detection of pedestrians. However, pedestrian detection suffers from occlusion and small target.
Shangtao You, Kai Zhu, Zhenchao Gu
core   +1 more source

The improved YOLOv5 model training results.

Workpiece surface defect detection is an indispensable part of intelligent production. The surface information obtained by traditional 2D image detection has some limitations due to the influence of environmental light factors and part complexity ...
Danya Zhang (15309566), Xuesong Huang (18468785), Ligang Qu (18468782), Zeng Chen (6910307)   +3 more
core   +1 more source

Detection and tracking of safety helmet wearing based on deep learning

Open Computer Science
Failure to wear a helmet correctly is a significant cause of injury or death in the construction industry and industrial production. Traditional supervision methods predominantly rely on manual oversight, incurring substantial costs and demonstrating ...
Liang Hua, Yang Liqin, Chen Jinhua, Liu Xin, Hang Guihua   +4 more
doaj   +1 more source

Object Detection Using YOLOv5 and OpenCV

Object detection is one of the main tasks in computer vision, aimed at recognizing and localizing objects in images or videos. In this study, we utilize the YOLOv5 model, which is well known for its efficiency in realtime object detection.
Subagja , Mifta, Rahman, Ben
core   +1 more source