Object Detection in Thermal Infrared Image Based on Improved YOLOX

Infrared image has received much attention, but the weak features and multi noise in it bring difficulties to object detection. In this letter, an improved YOLOX called YOLOX-IRI is proposed to improve the detection accuracy on infrared images. First, an improved convolutional block attention module...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2023, Vol.20, p.1-4
Main Authors: Gao, Ruijie, Cai, Zhanchuan
Format: Article
Language:English
Subjects:
Algorithms
Attention mechanism
Detection
Detectors
Feature extraction
Geoscience and remote sensing
Head
infrared image
Infrared imagery
Modules
Neck
Object detection
Object recognition
Training
YOLOX
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Summary:Infrared image has received much attention, but the weak features and multi noise in it bring difficulties to object detection. In this letter, an improved YOLOX called YOLOX-IRI is proposed to improve the detection accuracy on infrared images. First, an improved convolutional block attention module (CBAM) is proposed to make the network focus on the object area. This module enriches the feature information by mixing three kinds of pooling methods properly, which helps the network distinguish between background and object. Second, class-balanced loss is introduced to suppress adverse effects caused by unbalanced sample distribution problems. This loss function can balance the contribution of each class to the total loss by assigning weights, thereby improving the classification result. Experimental results indicate that our method is superior to other object detection algorithms.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2023.3312148