An improved small object detection method based on Yolo V3

In this paper, an improved algorithm based on Yolo V3 is proposed, which can effectively improve the accuracy of small target detection. First of all, the feature map acquisition network is improved. The image double-segmentation and bilinear upsampling network are used to replace the 2-step downsam...

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Bibliographic Details
Published in:Pattern analysis and applications : PAA 2021-08, Vol.24 (3), p.1347-1355
Main Authors: Xianbao, Cheng, Guihua, Qiu, Yu, Jiang, Zhaomin, Zhu
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Computer architecture
Computer Science
Convolution
Feature maps
Image acquisition
Image segmentation
Object recognition
Pattern Recognition
Short Paper
Target detection
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Summary:In this paper, an improved algorithm based on Yolo V3 is proposed, which can effectively improve the accuracy of small target detection. First of all, the feature map acquisition network is improved. The image double-segmentation and bilinear upsampling network are used to replace the 2-step downsampling convolution network in the original network architecture, and the feature values of large and small objects are amplified. Secondly, a size recognition module is added to the input image to reduce the loss of morpheme features caused by no-feature value filling and enhance the recognition ability of small objects. Thirdly, in order to avoid the gradient fading of the network, the residual network element of the output network layer is added to enhance the feature channel of small object detection. Compared with Yolo V3, our algorithm improves the detection accuracy of small objects from 82.4 to 88.5%, the recall rate from 84.6 to 91.3%, and the average accuracy from 95.5 to 97.3%, respectively.
ISSN:1433-7541
1433-755X
DOI:10.1007/s10044-021-00989-7