Journal Article10.1016/J.COMPAG.2019.01.012
Apple detection during different growth stages in orchards using the improved YOLO-V3 model
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TL;DR: The test results show that the proposed YOLOV3-dense model is superior to the original YOLO-V3 model and the Faster R-CNN with VGG16 net model, which is the state-of-art fruit detection model.
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About: This article is published in Computers and Electronics in Agriculture. The article was published on 01 Feb 2019.
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Citations
JUVDsi v1: developing and benchmarking a new still image database in Indian scenario for automatic vehicle detection
TL;DR: The aim of this research is to develop a still image database, named as JUVDsi v1, which includes nine different types of vehicle classes collected through mobile phone cameras in various ways for designing an automated traffic management system.
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A Deep Learning Approach to Identify Fresh and Stale Fruits and Vegetables with YOLO
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TL;DR: In this article , the authors focused on identifying if a fruit or vegetable is fresh or stale using two different versions of YOLO namely Yolov4 and YOLov5.
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Design and Analysis of Refined Inspection of Field Conditions of Oilfield Pumping Wells Based on Rotorcraft UAV Technology
TL;DR: A framework that combines You only look once version 3 (YOLOv3) and a sort algorithm to complete multi-target tracking in the form of tracking by detection is proposed that provides help for the real-time detection of the working condition, which has a strong practical application.
YOLOv4 Vs YOLOv5: Object Detection on Surveillance Videos
V. Jokanović
- 01 Jan 2023
TL;DR: In this paper , the authors implemented YOLO-v4 and YOLOLOv5 techniques of region-free approach due to their high detection speed and accuracy, which achieved 84% accuracy and gave better results than YOLOV4 which achieved 56% accuracy only.
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CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face
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TL;DR: CM-YOLOv8 is a lightweight object detection algorithm designed for the coal mining face, introducing adaptive predefined anchor boxes and a pruning method based on the L1 norm to enhance detection performance and reduce model volume and computation.
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