Self-Recurrent Learning and Gap Sample Feature Synthesis-Based Object Detection Method

Object detection-based deep learning by using the looking and thinking twice mechanism plays an important role in electrical construction work. Nevertheless, the use of this mechanism in object detection produces some problems, such as calculation pressure caused by multilayer convolution and redund...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical problems in engineering 2021-09, Vol.2021, p.1-15
Main Authors: Jiang, Lvjiyuan, Wang, Haifeng, Kai Yan, Zhou, Chengjiang, Li, Songlin, Dang, Junpeng, Chang, Rong, Peng, Jie, Fang, Yanbin, Dai, Chenkai, Yang, Yang
Format: Article
Language:English
Subjects:
Deep learning
Iterative methods
Modules
Multilayers
Neural networks
Object recognition
Semantics
Sensors
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Object detection-based deep learning by using the looking and thinking twice mechanism plays an important role in electrical construction work. Nevertheless, the use of this mechanism in object detection produces some problems, such as calculation pressure caused by multilayer convolution and redundant features that confuse the network. In this paper, we propose a self-recurrent learning and gap sample feature fusion-based object detection method to solve the aforementioned problems. The network consists of three modules: self-recurrent learning-based feature fusion (SLFF), residual enhancement architecture-based multichannel (REAML), and gap sample-based features fusion (GSFF). SLFF detects objects in the background through an iterative convolutional network. REAML, which serves as an information filtering module, is used to reduce the interference of redundant features in the background. GSFF adds feature augmentation to the network. Simultaneously, our model can effectively improve the operation and production efficiency of electric power companies’ personnel and guarantee the safety of lives and properties.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/2920062