The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Aug. 09, 2022
Filed:
May. 28, 2020
Jiangnan University, Wuxi, CN;
Wei Fang, Wuxi, CN;
Peiming Ren, Wuxi, CN;
Lin Wang, Wuxi, CN;
Jun Sun, Wuxi, CN;
Xiaojun Wu, Wuxi, CN;
Jiangnan University, Wuxi, CN;
Abstract
Disclosed is a real-time object detection method deployed on a platform with limited computing resources, which belongs to the field of deep learning and image processing. In the present invention, YOLO-v3-tiny neural network is improved, Tinier-YOLO reserves the front five convolutional layers and pooling layers of YOLO-v3-tiny and makes prediction at two different scales. Fire modules in SqueezeNet, 1×1 bottleneck layers, and dense connection are introduced, so that the structure is used to achieve smaller, faster, and more lightweight network that can be run in real time on an embedded AI platform. The model size of Tinier-YOLO in the present invention is only 7.9 MB, which is only ¼ of 34.9 MB of YOLO-v3-tiny, and ⅛ of YOLO-v2-tiny. The reduction in the model size of Tinier-YOLO does not affect real-time performance and accuracy of Tinier-YOLO. Real-time performance of Tinier-YOLO in the present invention is 21.8% higher than that of YOLO-v3-tiny and 70.8% higher than that of YOLO-v2-tiny. Compared with YOLO-v3-tiny, accuracy of Tinier-YOLO is increased by 10.1%. Compared with YOLO-v2-tiny, accuracy of Tinier-YOLO is increased by nearly 18.2%. Tinier-YOLO in the present invention can still achieve real-time detection on a platform with limited resources, and effects are better.