Tomato detection based on modified YOLOv3 framework.
Tomato detection based on modified YOLOv3 framework.
复制标题
基于改进的YOLOv3框架的番茄检测
DOI：
10.1038/s41598-021-81216-5
复制
发表时间：
2021-01-14
影响因子：
4.6
通讯作者：
Lawal MO
中科院分区：
文献类型：
--
作者：
Lawal MO
水果检测是机器人收获平台的重要组成部分。然而，不均匀的环境条件，如枝叶遮挡、光照变化、番茄丛生、遮挡等，使得水果检测变得非常困难。为了解决这些问题，采用了一种改进的YOLOv3模型--YOLO-Tomato模型来检测复杂环境条件下的番茄。在修正的YOLOv3模型中应用了标记你所看到的方法、密集的结构整合、空间金字塔合并和Mish功能激活，YOLO-Tomato模型：YOLO-Tomato-A在AP 98.3%，检测时间48ms，YOLO-Tomato-B在AP 99.3%，检测时间44ms，YOLO-Tomato-C在AP 99.5%，检测时间52ms，比其他最先进的方法更好。
Fruit detection forms a vital part of the robotic harvesting platform. However, uneven environment conditions, such as branch and leaf occlusion, illumination variation, clusters of tomatoes, shading, and so on, have made fruit detection very challenging. In order to solve these problems, a modified YOLOv3 model called YOLO-Tomato models were adopted to detect tomatoes in complex environmental conditions. With the application of label what you see approach, densely architecture incorporation, spatial pyramid pooling and Mish function activation to the modified YOLOv3 model, the YOLO-Tomato models: YOLO-Tomato-A at AP 98.3% with detection time 48 ms, YOLO-Tomato-B at AP 99.3% with detection time 44 ms, and YOLO-Tomato-C at AP 99.5% with detection time 52 ms, performed better than other state-of-the-art methods.
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影响因子：
3.9
作者：
Liu, Guoxu;Nouaze, Joseph Christian;Kim, Jae Ho
通讯作者：
Kim, Jae Ho
影响因子：
3.9
作者：
Liu, Guoxu;Mao, Shuyi;Kim, Jae Ho
通讯作者：
Kim, Jae Ho
影响因子：
19.5
作者：
Russakovsky, Olga;Deng, Jia;Fei-Fei, Li
通讯作者：
Fei-Fei, Li
DOI：
10.3390/s16081222
发表时间：
2016-08-03
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Sa I;Ge Z;Dayoub F;Upcroft B;Perez T;McCool C
通讯作者：
McCool C
DOI：
10.3965/j.ijabe.20140702.014
发表时间：
2014-04-01
影响因子：
2.4
作者：
Qiang, Lu;Cai Jianrong;Zhang Yajing
通讯作者：
Zhang Yajing