© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Small object detection is one of the challenging tasks in computer vision. Most of the existing small object detection models cannot fully extract the characteristics of small objects within an image, due to the small coverage area, low resolution and unclear detailed information of small objects in the image; hence, the effect of these models is not ideal. To solve this problem, a simple and efficient reinforce feature pyramid network R-FPN is proposed for the YOLOv5 algorithm. The learnable weight is introduced to show the importance of different input features, make full use of the useful information of different feature layers and strengthen the extraction of small object features. At the same time, a channel space mixed attention CSMA module is proposed to extract the detailed information of small objects combined with spaces and channels, suppress other useless information and further improve the accuracy of small object detection. The experimental results show that the proposed method improves the average accuracy AP, AP50 and AR100 of the original algorithm by 2.11%, 2.86% and 1.94%, respectively, and the detection effect is better than the existing small object detection algorithms, which proves the effectiveness of the proposed method.

詳細

タイトル
Small Object Detection Method Based on Weighted Feature Fusion and CSMA Attention Module
著者
Chao, Peng 1 ; Zhu, Meng 2 ; Ren, Honge 3 ; Emam, Mahmoud 4   VIAFID ORCIDロゴ 

 College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China 
 College of Information Engineering, Harbin University, Harbin 150086, China; Heilongjiang Forestry Intelligent Equipment Engineering Research Center, Harbin 150040, China 
 College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China; Heilongjiang Forestry Intelligent Equipment Engineering Research Center, Harbin 150040, China 
 Faculty of Artificial Intelligence, Menoufia University, Shebin El-Koom 32511, Egypt 
最初のページ
2546
出版年
2022
出版日
2022
出版社
MDPI AG
e-ISSN
20799292
リソースタイプ
学術誌
出版物の言語
English
ProQuest 文書 ID
2706171198
著作権
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.