Results 41 to 50 of about 5,295 (214)

YOLOv5s_2E: Improved YOLOv5s for Aerial Small Target Detection

IEEE Access, 2023
To address the issues of low accuracy in existing small object detection algorithms, an improved network model algorithm called YOLOv5s_2E is proposed. This method first uses the k-means++ clustering algorithm to calculate the prior boxes of the Visdrone dataset.
Tao Shi, Yao Ding, Wenxu Zhu
openaire   +1 more source

Accurate Detection and Precision Spraying of Corn and Weeds Using the Improved YOLOv5 Model

IEEE Access, 2023
The precise identification of corn and weeds plays an important role in precise spraying. This paper proposed a lightweight model based on the improved yolov5s and built a precision spraying robot.
Baoju Wang, Yu Yan, Yubin Lan, Meng Wang, Zhihao Bian   +4 more
doaj   +1 more source

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

Lacking data? No worries! How synthetic images can alleviate image scarcity in wildlife surveys: A case study with muskox (Ovibos moschatus)

Remote Sensing in Ecology and Conservation, EarlyView.
This study investigates the integration of synthetic imagery, created with diffusion‐based models, to supplement limited training data and improve muskox (Ovibos moschatus) detection in zero‐shot (ZS) and few‐shot (FS) settings. ZS models detected more than 80% of muskoxen in real images, confirming the potential of synthetic data as a substitute for ...
Simon Durand, Samuel Foucher, Alexandre Delplanque, Joëlle Taillon, Jérôme Théau   +4 more
wiley   +1 more source

Multi-strategy Collaborative Optimized YOLOv5s and its Application in Distance Estimation

, 2023
The increasing accident rate brought about by the explosive growth of automobiles has made the research on active safety systems of automobiles increasingly important.
Li, Xiangxiang, Mu, Zhenping, Shen, Zijian   +2 more
core  

IoT-Based Smoking Violation Detection System Equipped with Object Detection Using YOLOv5s Algorithm

Journal of Applied Informatics and Computing
Smoking is a common habit in Indonesia. The Indonesian government has implemented regulations on smoke-free areas, but violations of the smoke-free policy still often occur.
Audina Amalia Putri, Indra Hermawan
doaj   +1 more source

Deep learning‐based super‐resolution reconstruction and improved YOLOv9 for efficient benthos detection: a case study at Lake Hamana, Japan

Remote Sensing in Ecology and Conservation, EarlyView.
This study presents a UAV‐based framework that integrates deep learning‐based super‐resolution reconstruction and an enhanced YOLO detector to improve centimetre‐scale benthic organism monitoring. Using hermit crabs in Lake Hamana, a coastal lagoon in Japan, as a case study, the method substantially enhanced small‐object detection performance ...
Fan Zhao, Bangzhang Ma, Dianhan Xi, Jiaqi Wang, Yijia Chen, Yongying Liu, Xinlei Shao, Mowen Zhang, Guocheng Zhang, Jundong Chen, Katsunori Mizuno   +10 more
wiley   +1 more source

Towards Fully Autonomous UAV:Damaged Building-Opening Detection for Outdoor-Indoor Transition in Urban Search and Rescue [PDF]

Autonomous unmanned aerial vehicle (UAV) technology is a promising technology for minimizing human involvement in dangerous activities like urban search and rescue missions (USAR), both in indoor and outdoor.
Bergado, John Ray, Nex, Francesco, Surojaya, Ali, Zhang, Ning   +3 more
core   +1 more source

Comparing the Performance of Deep Learning Algorithms for Vehicle Detection and Classification [PDF]

, 2022
The rapid pace of developments in Artificial Intelligence (AI) provides unprecedented opportunities to enhance the performance of Intelligent Transportation Systems.
Arthur, Elizabeth
core  

Detection algorithm for wearing safety helmet under mine based on improved YOLOv5s

Meitan kexue jishu
Aiming at the problems of low accuracy and high missed detection rate of personnel safety helmet detection algorithm caused by complex environment under mine, an improved mine safety helmet detection algorithm based on YOLOv5s is proposed.
Yuanbin WANG, Sixiong WEI, Huaying WU, Yu DUAN, Meng LIU   +4 more
doaj   +1 more source