An improved approach for two-stage detection model

Jing Liu; Xiaolong Ma

doi:10.1117/12.2643051

12 October 2022 An improved approach for two-stage detection model

Jing Liu, Xiaolong Ma

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 1234208 (2022) https://doi.org/10.1117/12.2643051
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

With the rapid development of deep learning models, the performance of object detection have made great success in recent years. However, the problem of low detection efficiency still exists in two-stage detection model. In this paper, we design a lightweight fully convolution neural network(LFCNN) as backbone to extract features more efficiently. Firstly, LFCNN is a lightweight network with only a small number of network parameters, which ensures that it can complete the feature extraction task more quickly while maintaining detection accuracy. Secondly, LFCNN uses residual connection to ensure the performance of the deep network and uses dense connection to realize the reuse and fusion of multi-layer features of the network, which significantly improve the detection accuracy. Moreover, we also come up with a novel method called anchor scale generator(ASG) to obtain the proper predefined anchor scales for generating more accurate region proposals, which further enhances localization ability of objects. A large number of experiments on Pascal VOC and COCO datasets show that our approach is superior to other methods in both bounding boxes localization accuracy and detection performance.

Citation Download Citation

Jing Liu and Xiaolong Ma "An improved approach for two-stage detection model", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 1234208 (12 October 2022); https://doi.org/10.1117/12.2643051

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KEYWORDS

Sensors

Convolution

Feature extraction

Detection and tracking algorithms

Network architectures

Performance modeling

Neural networks

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