Open AccessData enhancement method for object detection
Author(s) -
Zhijian Yin,
Lei Xu,
HanQing Yu,
Zhen Yang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2209/1/012027
Subject(s) - computer science , artificial intelligence , minimum bounding box , object detection , intersection (aeronautics) , bounding overwatch , object (grammar) , image (mathematics) , brightness , pattern recognition (psychology) , rotation (mathematics) , random forest , computer vision , contrast enhancement , sensor fusion , medicine , magnetic resonance imaging , radiology , physics , optics , engineering , aerospace engineering
A data enhancement method for object detection is proposed to address the problem that there are few data enhancement means for target detection. This method has obvious advantages for small targets and scenarios where the target background is lacking. It is also a method to solve the problem of small and unbalanced samples. In this paper, the object is subtracted from the bounding box, and after processing, it is fused into a new image using various image classification improvement means such as random brightness, random contrast, random rotation, random cropping, etc. Using mixup-like fusion methods, and XIOU is proposed to guarantee the t intersection ratio of the target synthesis during synthesis. An improvement of 2% is obtained on yolov3 over the VOC dataset.