Results 61 to 70 of about 10,507 (209)
An Improvement of the Fire Detection and Classification Method Using YOLOv3 for Surveillance Systems
Italian National Conference on Sensors, 2021 Currently, sensor-based systems for fire detection are widely used worldwide. Further research has shown that camera-based fire detection systems achieve much better results than sensor-based methods. In this study, we present a method for real-time high-
A. Abdusalomov +3 more
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Mixed YOLOv3-LITE: A Lightweight Real-Time Object Detection Method
Sensors, 2020 Embedded and mobile smart devices face problems related to limited computing power and excessive power consumption. To address these problems, we propose Mixed YOLOv3-LITE, a lightweight real-time object detection network that can be used with non ...
Haipeng Zhao +6 more
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Transfer Learning-Based YOLOv3 Model for Road Dense Object Detection
Information, 2023 Stemming from the overlap of objects and undertraining due to few samples, road dense object detection is confronted with poor object identification performance and the inability to recognize edge objects.
Chunhua Zhu, Jiarui Liang, Fei Zhou
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Study on Improvement of YOLOv3 Algorithm
Journal of Physics: Conference Series, 2021 Abstract In order to optimize the problem of wrong detection and missed detection of small targets in complex environment, a target detection algorithm of YOLOv3-SPP5 was proposed. YOLOv3 in the deep learning algorithm has achieved excellent detection effect in target detection, but it is not perfect in the complex environment.
Xinchao Liu, Haiyun Gan, Ying Yan
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A surface defect detection method of steel plate based on YOLOV3
Metalurgija, 2023 At present, the steel plate surface defect detection technology based on machine vision and convolutional neural network (CNN) has achieved good results.
G. Z. Ouyang, W. Y. Zhang
doaj
Spatial Attention Based Real-Time Object Detection Network for Internet of Things Devices
IEEE Access, 2020 Target detection algorithms for Internet of things (IoT) devices often require both high real-time performance and low computational complexity. Real-time object detection network: You Only Look Once Version 3 (YOLOv3) makes full use of multi-scale ...
Yongxin Zhang +3 more
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Multi-object detection method for vehicles based on improved YOLOv3 model
Xi'an Gongcheng Daxue xuebao, 2021 To solve the problems of low detection rate and poor robustness of near and far object on real road environment, YOLOv3-Y based on the Darknet-53 feature extraction network model was proposed.
Liping MA +3 more
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YOLOv3: An Incremental Improvement
, 2018 We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster.
Redmon, Joseph, Farhadi, Ali
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International Journal of Innovative Research in Computer Science and TechnologyThe object detection field has received significant improvement through deep learning technology and YOLO (You Only Look Once) stands out as a leading model which delivers fast and precise real-time results. This research evaluates the performance of five object detection models including YOLOv3 and ResNet101-based YOLOv3 (R-YOLO) and EfficientNetB0 ...
Jashanpreet Singh, Rajiv Kumar
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Scientific ReportsIn order to address the challenges of identifying, detecting, and tracking moving objects in video surveillance, this paper emphasizes image-based dynamic entity detection.
Maisy L. Cheng, Mengyuan Liu
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