Author(s): Xi, XL (Xi, Xiaoli); Wang, JX (Wang, Jinxin); Li, F (Li, Fang); Li, DM (Li, Dongmei)

Source: ELECTRONICS Volume: 11 Issue: 14 Article Number: 2154 DOI: 10.3390/electronics11142154 Published: JUL 2022

Abstract: Detecting small objects in infrared images remains a challenge because most of them lack shape and texture. In this study, we proposed an infrared small-object detection method to improve the capacity for detecting thermal objects in complex scenarios. First, a sparse-skip connection block is proposed to enhance the response of small infrared objects and suppress the background response. This block is used to construct the detection model backbone. Second, a region attention module is designed to emphasize the features of infrared small objects and suppress background regions. Finally, a batch-averaged biased classification loss function is designed to improve the accuracy of the detection model. The experimental results show that the proposed small-object detection framework significantly increases precision, recall, and F1-score, showing that, compared with the current advanced detection models for small-object detection, the proposed detection framework has better performance in infrared small-object detection under complex backgrounds. The insights gained from this study may provide new ideas for infrared small object detection and tracking.

Accession Number: WOS:000832276100001

Author Identifiers:

Author        Web of Science ResearcherID        ORCID Number

xi, xiao li                  0000-0002-7242-5695

eISSN: 2079-9292

Full Text: https://www.mdpi.com/2079-9292/11/14/2154