Fish feeding intensity quantification using machine vision and a lightweight 3D ResNet-GloRe network
文献类型： 外文期刊
作者： Feng, Shuangxing 1 ; Yang, Xinting 2 ; Liu, Yang 2 ; Zhao, Zhengxi 2 ; Liu, Jintao 2 ; Yan, Yujie 5 ; Zhou, Chao 2 ;
作者机构： 1.Guangxi Univ, Sch Mech Engn, Nanning 530004, Peoples R China
2.Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R China
3.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China
4.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
5.Southeast Univ, Sch Automat, Nanjing 210096, Peoples R China
6.Middle Rd 9,Haidian Dist, Beijing 100097, Peoples R China
关键词： Feeding intensity quantification; Action positioning; 3D ResNet-GloRe; Lightweight
期刊名称：AQUACULTURAL ENGINEERING （ 影响因子：3.273； 五年影响因子：3.585 ）
ISSN： 0144-8609
年卷期： 2022 年 98 卷
页码：
收录情况： SCI
摘要： Quantifying feeding intensity of fish is important in developing intelligent feeding control system, thus improving feed utilization rate and reducing water pollution. The current study explored a real-time, high precision and lightweight 3D ResNet-Glore fish feeding intensity quantification network, which can accurately locate the four levels of fish feeding intensities in video stream. In this network, the lightweight GloRe module is expanded in 3D space, and the Residual block in the 3D ResNet network is modified to form the 3D GloRe module. The relational reasoning is achieved through graph convolution in the interactive space to improve accuracy of discrimination. In addition, the sliding window and the frame extraction processing of the video data significantly reduces the model parameters and the amount of calculation. Experimental results showed that the classification accuracy for four types of feeding intensity was 92.68%, which is 4.88% higher compared with that of the classical 3D ResNet network. The parameters were decreased by 46.08% and the GFLOPs decreased by 44.10%. The proposed network improved the training and recognition speed and reduced the hardware equipment requirements, which can provide a theoretical basis for subsequent feeding decisions.
- 相关文献
作者其他论文 更多>>
-
2D/0D Heterojunction Fluorescent Probe with Schottky Barrier Based on Ti3C2TX MXene Loaded Graphene Quantum Dots for Detection of H2S During Food Spoilage
作者：Jia, Zhixin;Ji, Zengtao;Yang, Xinting;Shi, Ce;Jia, Zhixin;Yang, Xinting;Shi, Ce;Sun, Xia;Guo, Yemin;Jia, Zhixin;Ji, Zengtao;Yang, Xinting;Shi, Ce;Jia, Zhixin;Ji, Zengtao;Yang, Xinting;Shi, Ce;Jia, Zhixin;Ji, Zengtao;Yang, Xinting;Shi, Ce;Zhang, Jingbin;Zhang, Jingbin;Zhang, Jiaran
关键词：fluorescent probe; graphene quantum dots; H2S contamination; heterojunction; Ti3C2Tx MXene
-
DF-DETR: Dead fish-detection transformer in recirculating aquaculture system
作者：Fu, Tingting;Feng, Dejun;Li, Shantan;Fu, Tingting;Ma, Pingchuan;Hu, Weichen;Yang, Xinting;Li, Shantan;Zhou, Chao;Fu, Tingting;Ma, Pingchuan;Hu, Weichen;Yang, Xinting;Li, Shantan;Zhou, Chao;Fu, Tingting;Ma, Pingchuan;Hu, Weichen;Yang, Xinting;Li, Shantan;Zhou, Chao
关键词：DF-DETR; Dead fish detection; Feature fusion; Recirculating aquaculture system
-
DAMI-YOLOv8l: A multi-scale detection framework for light-trapping insect pest monitoring
作者：Chen, Xiao;Hu, Huan;Li, Tianjun;Chen, Xiao;Yang, Xinting;Hu, Huan;Li, Tianjun;Zhou, Zijie;Li, Wenyong;Chen, Xiao;Yang, Xinting;Hu, Huan;Li, Tianjun;Zhou, Zijie;Li, Wenyong;Chen, Xiao;Yang, Xinting;Hu, Huan;Li, Tianjun;Zhou, Zijie;Li, Wenyong;Zhou, Zijie
关键词：Pest detection; YOLOv8; Fusion features; Small objects; Multiple scale detection
-
Enhancing potato leaf protein content, carbon-based constituents, and leaf area index monitoring using radiative transfer model and deep learning
作者：Feng, Haikuan;Fan, Yiguang;Ma, Yanpeng;Liu, Yang;Chen, Riqiang;Bian, Mingbo;Fan, Jiejie;Yang, Guijun;Zhao, Chunjiang;Feng, Haikuan;Zhao, Chunjiang;Yue, Jibo;Fu, Yuanyuan;Leng, Mengdie;Jin, Xiuliang;Zhao, Yu
关键词：Potato; Deep learning; Radiative transfer model; Transfer learning; Leaf protein content
-
Segmentation and Fractional Coverage Estimation of Soil, Illuminated Vegetation, and Shaded Vegetation in Corn Canopy Images Using CCSNet and UAV Remote Sensing
作者：Zhang, Shanxin;Yue, Jibo;Shu, Meiyan;Zhang, Shanxin;Wang, Xiaoyan;Feng, Haikuan;Feng, Haikuan;Liu, Yang
关键词：segmentation; digital camera; corn; deep learning
-
Estimation of potato above-ground biomass based on the VGC-AGB model and deep learning
作者：Feng, Haikuan;Fan, Yiguang;Bian, Mingbo;Liu, Yang;Chen, Riqiang;Ma, Yanpeng;Fan, Jiejie;Yang, Guijun;Zhao, Chunjiang;Yue, Jibo;Feng, Haikuan;Zhao, Chunjiang
关键词：Hyperspectral; Above-ground biomass; Potato; Deep learning; Leaf area index
-
Utilizing UAV-based hyperspectral remote sensing combined with various agronomic traits to monitor potato growth and estimate yield
作者：Liu, Yang;Feng, Haikuan;Fan, Yiguang;Fan, Jiejie;Ma, Yanpeng;Chen, Riqiang;Bian, Mingbo;Yang, Guijun;Liu, Yang;Yue, Jibo;Yang, Fuqin
关键词：Crop growth monitoring; Potato yield; Crop traits; UAV; Hyperspectral
