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Domain Adaptation for Agricultural Image Recognition and Segmentation Using Category Maps
Kota Takahashi,Hirokazu Madokoro,Satoshi Yamamoto,Yo Nishimura,Stephanie Nix,Hanwool Woo,Takashi K. Saito,Kazuhito Sato 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
Recognition accuracy obtained using deep learning drops precipitously when the training data are insufficient. This paper presents a data-expansion method for training of the transfer learning source domain. Using expanding images generated from weights on a category map as source data, we compared accuracies obtained from five derivative models and our previously reported method. Moreover, we obtained the result of domain adaptation between actual images and synthetic images using weights obtained during transfer learning. Based on those results, we verify whether the amount of training data can be expanded quantitatively and qualitatively. Experiment results obtained from two open benchmark datasets and our original benchmark dataset demonstrated that our proposed method outperforms the previous method under a guarantee of sufficient accuracy for the synthetic images.
Calibration and 3D Reconstruction of Images Obtained Using Spherical Panoramic Camera
Hirokazu Madokoro,Satoshi Yamamoto,Yo Nishimura,Stephanie Nix,Hanwool Woo,Kazuhito Sato 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
This study was conducted to develop a 3D reconstruction procedure for application to crop monitoring. For 3D construction of a similar target object, we compared images obtained from two camera types: a compact digital camera (CDC) and a spherical panoramic camera (SPC). First, we calculate camera parameters from images that include a checkerboard. Subsequently, we correct the image distortion including that of the target object using the camera parameters. Finally, we estimate camera positions and three-dimensional (3D) reconstruction based on the structure from motion (SfM). Experimentally obtained results demonstrated that the 3D reconstruction of a target object was improved after calibration compared with that before calibration. Moreover, we conducted an application experiment using a tree in an outdoor environment as a trial of practical use at a farm.
Enhanced Thermal and Mechanical Properties of Polyimide/Graphene Composites
Wen Dai,Jinhong Yu,Yi Wang,Yingze Song,Hua Bai,Kazuhito Nishimura,Huiwei Liao,Nan Jiang 한국고분자학회 2014 Macromolecular Research Vol.22 No.9
Polyimide (PI)/graphene sheets (GSs) composites were prepared by solution blending. The incorporationof GSs into PI increased the thermal conductivity, thermal stability and mechanical properties of PI. The thermal conductivityof PI/GSs composites was significantly improved compared with that of neat PI from 0.254 to 1.002 W/mK; this can be attributed to the homogeneous dispersion of graphene and the formation of heat conduction pathway. Furthermore, the Young modulus of PI/GSs was raised up to 4.04 GPa, approximately two-fold enhancement incomparison with that of neat PI. In addition, the incorportation of GSs in PI indicated excellent optical transparencyat the lowest weight fractions of GSs and modified wettability of PI films.