Results 111 to 120 of about 4,232 (214)

Research on the Detection Method of Safflower Filaments in Natural Environment Based on Improved YOLOv5s

IEEE Access
The accurate and rapid identification of safflower filaments is a prerequisite for automating harvesting. This paper proposes a lightweight, high-precision detection model for safflower filaments based on YOLOv5s, named YOLOv5s-MCD, to address the issues
Bangbang Chen, Feng Ding, Baojian Ma, Xiangdong Liu, Shanping Ning   +4 more
doaj   +1 more source

Study of methods for searching and localizing objects in images from aircraft using convolutional neural networks

Радіоелектронні і комп'ютерні системи
The use of unmanned and manned aerial vehicles for remote object localization and classification is very common. These methods are used in various systems, ranging from territory surveys to law enforcement.
Rostyslav Tsekhmystro, Oleksii Rubel, Vladimir Lukin   +2 more
doaj   +1 more source

Improved Vehicle Object Detection Algorithm Based on Swin-YOLOv5s

In response to the challenges of low detection accuracy, slow speed, and high rates of false positives and missed detections in existing YOLOv5s vehicle detection models under complex traffic scenarios, an improved Swin-YOLOv5s vehicle detection ...
Meiqin Liu, Haichao An, Jianhua Tang, Ying Fan   +3 more
core   +1 more source

Marine fish species recognition based on improved YOLOv5s

Shipin yu jixie
ObjectiveIn order to improve the recognition accuracy of different kinds of marine fish, an improved YOLOv5s marine fish species recognition method was proposed.MethodsK⁃means++algorithm was used to cluster the real frames of marine fish, and more ...
ZHANG Haifeng, LU Xinchun, FENG Bo, YANG Jin   +3 more
doaj   +1 more source

Multi-Head Self-Attention-Incorporated YOLOv5s for Satellites Detection

To address challenges associated with detecting objects in images for in-orbit services, often involving changes in the object scale, we proposed a method for detecting satellites using YOLOv5s with a multi-head self-attention mechanism MHSA-YOLOv5s ...
Yuan Ren, Yufei Liu, Weikun Pang, Weijie Wang, Lifen Wang, Ruida Ye   +5 more
core   +1 more source

Large block coal detection algorithm for fully mechanized working face based on MES-YOLOv5s

Gong-kuang zidonghua
The objects in the fully mechanized working face have the features of high-speed motion, multi-scale, occlusion, etc. The existing object detection algorithms have problems such as low precision, large memory of models, and strong hardware dependence. In
XU Ciqiang, JIA Yunhong, TIAN Yuan
doaj   +1 more source

Lightweight ship detection method based on YOLO-FNC model

Zhongguo Jianchuan Yanjiu
ObjectiveA lightweight and efficient ship detection method based on the YOLO-FNC model is proposed for complex environments such as ports with dense traffic. MethodFirst, a FasterNeXt neural network module is designed on the basis of the FasterNet method
Bingyan ZHANG, Chuang ZHANG, Zhennan SHI, Songtao LIU   +3 more
doaj   +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

CBG-YOLOv5s detection effect diagram.

The automatic detection of the degree of surface corrosion on metal structures is of significant importance for assessing structural damage and safety.
Zhitong Jia (17709113), Lingzhi Wu (133076), Zhendong Cui (18340284), Mingjiao Fu (18340281)   +3 more
core   +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