Results 31 to 40 of about 5,295 (214)

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

Detection of Broken Hongshan Buckwheat Seeds Based on Improved YOLOv5s Model

Agronomy, 2023
Breeding technology is one of the necessary means for agricultural development, and the automatic identification of poor seeds has become a trend in modern breeding.
Xin Li, Wendong Niu, Yinxing Yan, Shixing Ma, Jianxun Huang, Yingmei Wang, Renjie Chang, Haiyan Song   +7 more
doaj   +1 more source

Child Detection Model Using YOLOv5 [PDF]

, 2023
CCTV surveillance systems have been installed in public locations and are used to search for missing children and fight crime. The Penang City Council has deployed a face recognition CCTV monitoring system.
Ahmad Khalid, Shamsul Kamal, Mohd Fadzil, Lokman, Tahir, Azrina   +2 more
core   +1 more source

Multiple structural defect detection for reinforced concrete buildings using YOLOv5s

, 2022
Building inspection and maintenance are becoming increasingly essential means by which to consider the deterioration problems of old, reinforced concrete (RC) buildings.
Li, C, Pan, W, Su, RKL, Yuen, PC
core   +2 more sources

Optimized Yolov5s-Im for real-time apple flower detection in drone-based pollination

Smart Agricultural Technology
As traditional pollinators face increasing threats from climate change, the development of robotic pollination technology has become imperative, with apple flower detection emerging as a critical component of the technology.
Shahram Hamza Manzoor, Zhao Zhang, Hongwen Li, Qu Zhang, Kuifan Chen, C. Igathinathane, Tianzhong Li, Wei Li, Muhammad Naveed Tahir, Nabil Mustafa, Mustafa Mhamed   +10 more
doaj   +1 more source

SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network

, 2023
The increasing prevalence of unmanned aerial vehicles (UAVs), commonly known as drones, has generated a demand for reliable detection systems. The inappropriate use of drones presents potential security and privacy hazards, particularly concerning ...
Alam, Muhammad Zeshan, Almasoud, Abdullah M., Dil, Mahnoor, Kaleem, Zeeshan, Khan, Misha Urooj, Orakazi, Farooq Alam, Yuen, Chau   +6 more
core  

HIC-YOLOv5: Improved YOLOv5 For Small Object Detection

2024 IEEE International Conference on Robotics and Automation (ICRA)
Small object detection has been a challenging problem in the field of object detection. There has been some works that proposes improvements for this task, such as adding several attention blocks or changing the whole structure of feature fusion networks.
Tang, Shiyi, Zhang, Shu, Fang, Yini
openaire   +2 more sources

PENERAPAN DETEKSI KECACATAN BUAH JERUK MENGGUNAKAN MODEL DEEP LEARNING YOLOV5 [PDF]

Penentuan kualitas buah jeruk secara manual cenderung tidak konsisten serta memerlukan waktu dan tenaga yang signifikan. Oleh karena itu, penelitian ini bertujuan untuk merancang sistem otomatis dalam mendeteksi kecacatan buah jeruk dengan memanfaatkan ...
Adianto, Aldi Febriansyah, Muhammad Khoirul Hasin, Putri Nur Rahayu, Rinanto, Noorman   +4 more
core   +2 more sources

Research on coal gangue detection in coal preparation plant based on YOLOv5s-FSW model

Gong-kuang zidonghua
A coal gangue detection method in coal preparation plant based on YOLOv5s-FSW model is proposed to address the problems of insufficient feature extraction, large parameter quantity, low detection precision, and poor real-time performance in existing coal
YAN Bijuan, WANG Kaimin, GUO Pengcheng, ZHENG Xinxu, DONG Hao, LIU Yong   +5 more
doaj   +1 more source

KPE-YOLOv5: An Improved Small Target Detection Algorithm Based on YOLOv5

Electronics, 2023
At present, the existing methods have many limitations in small target detection, such as low accuracy, a high rate of false detection, and missed detection. This paper proposes the KPE-YOLOv5 algorithm aiming to improve the ability of small target detection. The algorithm has three improvements based on the YOLOv5 algorithm.
Yang, Rujin, Li, Wenfa, Shang, Xinna, Zhu, Deping, Man, Xunyu   +4 more
openaire   +1 more source