Journal Article10.1016/j.engappai.2023.106217
Lightweight object detection algorithm for robots with improved YOLOv5
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TL;DR: Zhang et al. as mentioned in this paper proposed a lightweight object detection algorithm for robots with an improved YOLOv5 backbone to reduce the amount of processing required for feature extraction and increase the speed of detection.
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About: This article is published in Engineering Applications of Artificial Intelligence. The article was published on 01 Aug 2023. The article focuses on the topics: Computer science & Robot.
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Citations
Deep learning-based object detection in maritime unmanned aerial vehicle imagery: Review and experimental comparisons
Chenjie Zhao,Ryan Wen Liu,Jingxiang Qu,Ruobin Gao +3 more
TL;DR: This work briefly summarizes four challenges for object detection on maritime UAVs, i.e., object feature diversity, device limitation, maritime environment variability, and dataset scarcity, and focuses on computational methods to improve Maritime UAV-based object detection performance in terms of scale-aware, small object detection, view-aware and rotated object detection.
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An enhanced real-time human pose estimation method based on modified YOLOv8 framework
Guodong Du
TL;DR: The backbone and neck of the YOLOv8x-pose real-time HPE model is improved and the context coordinate attention module (CCAM) is introduced to augment the model’s focus on salient features, reduce background noise interference, alleviate key point regression failure caused by limb occlusion, and improve the accuracy of pose estimation.
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An Improved Lightweight YOLOv5 Algorithm for Detecting Strawberry Diseases
01 Jan 2023
TL;DR: In this article , the ghost convolution (GhostConv) module is incorporated into the YOLOv5 network, reducing the parameter numbers and floating-point operations for extracting feature information using the backbone network.
LiteYOLO-ID: A Lightweight Object Detection Network for Insulator Defect Detection
Dahua Li,Yu-Sian Lu,Qiang Gao,X. G. Li,Xiao Yu,Yu Song +5 more
TL;DR: A lightweight insulator defect detection model based on an improved YOLOv5s, named LiteYOLO-ID, which meets the real-time detection requirements of insulator defects and is validated on the Pascal VOC dataset and the SFID dataset.
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Improved YOLOv8 Model for a Comprehensive Approach to Object Detection and Distance Estimation
Zu Jun Khow,Yi-Fei Tan,Hezerul Abdul Karim,Hairul Azhar Abdul Rashid +3 more
TL;DR: Improved YOLOv8 model for object detection and distance estimation achieves significant performance gains and showcases promising results in object detection and distance estimation tasks.
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