Evaluating YOLO Models for Grape Moth Detection in Insect Traps-Reference-Cited by-同舟云学术

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Evaluating YOLO Models for Grape Moth Detection in Insect Traps

Published:2023-07-16 Issue: Volume: Page:
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Container-title:IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Teixeira Ana Cláudia¹,Carneiro Gabriel¹,Morais Raul¹,Sousa Joaquim J.¹,Cunha António¹

Affiliation:

1. University of Trás-os-Montes and Alto Douro,Vila Real,Portugal,5000-801

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10281394/10281399/10282249.pdf?arnumber=10282249

Reference15 articles.

1. YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications;li,2022

2. Improving yolov5 with attention mechanism for detecting boulders from planetary images;zhu;Remote Sensing (Basel),2021

3. YOLO by Ultralytics (Version 8.0.0);jocher;Computer Software,2023

4. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors;wang,2022

5. Robust one-stage object detection with location-aware classifiers

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Improved Breast Cancer Detection from Ultrasound Images Using YOLOv8 Model;2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC);2023-11-24

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