[1]孙俊洋,符运来,吕晶,等.基于改进YOLOv7模型的海参苗计数方法研究[J].计算机技术与发展,2024,34(11):166-171.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0233]
　SUN Jun-yang,FU Yun-lai,LYU Jing,et al.Study on Counting Method of Sea Cucumber Seedlings Based on Improved YOLOv7 Model[J].,2024,34(11):166-171.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0233]
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基于改进YOLOv7模型的海参苗计数方法研究()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年11期
页码:
166-171
栏目:
人工智能
出版日期:
2024-11-10

文章信息/Info

Title:
Study on Counting Method of Sea Cucumber Seedlings Based on Improved YOLOv7 Model
文章编号:
1673-629X(2024)11-0166-06
作者:
孙俊洋1; 2; 符运来1; 2; 吕晶1; 2; 常立群1; 2; 李双双3
1. 大连海洋大学 信息工程学院,辽宁 大连 116023;2. 设施渔业教育部重点实验室(大连海洋大学),辽宁 大连 116023;3. 大连鑫玉龙海洋生物种业科技股份有限公司,辽宁 大连 116222
Author(s):
SUN Jun-yang1; 2; FU Yun-lai1; 2; LYU Jing1; 2; CHANG Li-qun1; 2; LI Shuang-shuang3
1. School of Information Engineering,Dalian Ocean University,Dalian 116023,China;2. Key Laboratory of Facility Fisheries, Ministry of Education (Dalian Ocean University),Dalian 116023,China;3. Dalian Xinyulong Marine Biological Seed Technology Co. ,Ltd. ,Dalian 116222,China
关键词:
卷积神经网络; 目标检测; 空间注意力; YOLOv7; 海参苗计数
Keywords:
convolutional neural network; object detection; spatial attention; YOLOv7; sea cucumber seedling count
分类号:
TP391.41;S917.41
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0233
摘要:
针对海参幼苗体型小、出售量大但计数不方便的问题,以大连市鑫玉龙养殖基地为样本采集地,构建了海参幼苗数据集。 针对 YOLOv7 对海参幼苗识别精度不高的问题,对 YOLOv7 原生网络进行改进,添加了注意力机制;并对卷积神经网络模型进行改进,在卷积的输入特征图中新增对应的通道来表征特征图像素点的坐标,让卷积学习过程中能够一定程度感知坐标来提升检测精度;添加轻量化上采样算子,利用输入特征图来预测上采样核,每个位置具有不同的上采样核,然后基于预测的上采样核进行特征重组;提升小个体海参的识别精度。 实验结果显示,基于改进 YOLOv7 的海参检测平均准确率为 90. 68% ,比 YOLOv3 提高了 9. 79 百分点,比 Mask R-CNN 提高了 6. 48 百分点,比 SSD 提高了 5. 35 百分点,比 YOLOv7 原模型提高了 1. 46 百分点。 结果表明,该方法能用于实现海参计数识别。
Abstract:
In order to solve the problem of small size and inconvenient counting of sea cucumber seedlings,the data set of sea cucumber seedlings was constructed with Dalian Xin Yulong Breeding base as the sample collection site. For the low recognition accuracy of sea cucumber seedlings,YOLOv7 native network was improved,attention mechanism was added. The convolutional neural network model was improved,and the corresponding channel was added to represent the coordinates of the pixels in the convolution learning process to improve the detection accuracy,so that the coordinates can be sensed to a certain extent in the process of convolutional learning to improve the detection accuracy. A lightweight upsampling operator was added to predict the upsampling kernel using the input feature map. Each position had a different upsampling kernel, and then the feature recombination was carried out based on the predicted upsampling kernel. The recognition accuracy of small individual sea cucumber was improved. The test results showed that the average accuracy of sea cucumber detection based on improved YOLOv7 was 90. 68% ,which was 9. 79 percentage points higher than that of YOLOv3,6. 48 percentage points higher than that of Mask R-CNN,5. 35 percentage points higher than that of SSD,and 1. 46 percentage points higher than that of the original YOLOv7. It was showed that the proposed method can be used to realize the sea cucumber count i-dentification.

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更新日期/Last Update: 2024-11-10