公开(公告)号：US20230213457A1

公开(公告)日：2023-07-06

申请号：US18069954

申请日：2022-12-21

Applicant: FENG CHIA UNIVERSITY

Inventor： Yee Siang GAN ,  Sze-Teng LIONG ,  Shih-Kai FAN ,  Che-Ming LI ,  Yu-Hsien LIN

IPC: G01N21/88 ,  G06T7/00

CPC classification number: G01N21/8851 ,  G06T7/0004 ,  G01N2021/8841 ,  G01N2021/888

Abstract: An automated optical double-sided inspection apparatus includes a first image-capturing portion, a second image-capturing portion, a platform, a first light-blocking portion, a second light-blocking portion, and a processing portion. The platform carries an external object. When the processing portion operates in a first capturing mode, the second light-blocking portion blocks visible light from passing therethrough, while the first light-blocking portion allows visible light to pass therethrough, so that the first image-capturing portion shoots a first side of the external object through the first light-blocking portion to obtain a first image. When the processing portion operates in a second capturing mode, the first light-blocking portion blocks visible light from passing therethrough, while the second light-blocking portion allows visible light to pass therethrough, so that the second image-capturing portion shoots a second side of the external object through the second light-blocking portion to obtain a second image.

公开(公告)号：US20230210102A1

公开(公告)日：2023-07-06

申请号：US18145015

申请日：2022-12-21

Applicant: FENG CHIA UNIVERSITY

Inventor： Yee Siang GAN ,  Shih-Kai FAN ,  Ching-Yun TSENG ,  Sze-Teng LIONG ,  Yu-Ting SHENG

IPC: A01M1/02 ,  G06V40/10 ,  G06V10/774 ,  G06T7/246 ,  A01M1/08

CPC classification number: A01M1/026 ,  G06V40/10 ,  G06V10/774 ,  G06T7/246 ,  A01M1/08 ,  G06T2207/20081

Abstract: An intelligent Forcipomyia taiwana monitoring and management system comprises: a catching mechanism grabbing a to-be-identified target; a database storing a datum comprising pictures of a flying insect category; a model training module using the pictures to establish a training model; an image capture module shooting an image including the target; an identifying module selecting a first segmented region including the target by using YOLO detection framework technology, extracting a first identification feature from the target, and inputting the feature into the training model for deep learning to identify a flying insect category to which the target belongs and produce an identification result; a counting module recording a number of the target into the database; and a predictive tracking module obtaining a marked object based on the result marked with the target identified in the image, and using a Monte-Carlo category algorithm to track and predict the object.