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HS-YOLO: Small Object Detection for Power Operation Scenarios

Authors :
Zhiwei Lin
Weihao Chen
Lumei Su
Yuhan Chen
Tianyou Li
Source :
Applied Sciences, Vol 13, Iss 19, p 11114 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Object detection methods are commonly employed in power safety monitoring systems to detect violations in surveillance scenes. However, traditional object detection methods are ineffective for small objects that are similar to the background information in the power monitoring scene, which consequently affects the performance of violation behavior detection. This paper proposed a small object detection algorithm named HS-YOLO, based on High-Resolution Network (HRNet) and sub-pixel convolution. First, to fully extract the microfeature information of the object, a small object feature extraction backbone network is proposed based on the HRNet structure. The feature maps of different scales are processed by multiple parallel branches and fused with each other in the network. Then, to fully retain the effective features of small objects, the sub-pixel convolution module is incorporated as the upsampling operator in the feature fusion network. The low-resolution feature map is upsampled to a higher resolution by reorganizing pixel values and performing padding operations in this module. On our self-constructed power operation dataset, the HS-YOLO algorithm achieved a mAP of 87.2%, which is a 3.5% improvement compared to YOLOv5. Particularly, the dataset’s AP for detecting small objects such as cuffs, necklines, and safety belts is improved by 10.7%, 5.8%, and 4.4%, respectively. These results demonstrate the effectiveness of our proposed method in detecting small objects in power operation scenarios.

Details

Language :
English
ISSN :
20763417
Volume :
13
Issue :
19
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.696ce2531e5c48bda53ac5eb1428937a
Document Type :
article
Full Text :
https://doi.org/10.3390/app131911114