Assessment algorithm of scene complexity based on X-CENet

Fanshu Shen; Yan Wen; Zhengrong Zuo

doi:10.1117/12.2653877

18 November 2022 Assessment algorithm of scene complexity based on X-CENet

Fanshu Shen, Yan Wen, Zhengrong Zuo

Proceedings Volume 12473, Second International Conference on Optics and Communication Technology (ICOCT 2022); 124731D (2022) https://doi.org/10.1117/12.2653877
Event: Second International Conference on Optics and Communication Technology (ICOCT 2022), 2022, Hefei, China

Abstract

In order to realize the rapid perception of complex scenes, the traditional scene complexity assessment algorithm has strong limitations in feature representation and scope of application, and it is difficult to deal with complex scenes. However, the existing deep network methods are lack of the consideration of the correlation between the underlying features of gray image and the complexity level, and the amount of parameters is too high to meet the needs of rapid response in practical applications. Based on the deep separable convolution module and residual connection structure, this paper designs a lightweight complexity assessment network X-CENet with stronger feature expression ability. A dense connection module which makes full use of multi-level features is introduced to improve the feature expression ability of the network for scene images. The underlying information such as image texture is particularly important for the assessment of complexity, so the feature cascade layer of the head and tail of the main modules is added to strengthen the utilization of the underlying feature information in the network. Experiments show that compared with other deep networks, this method can obtain higher assessment accuracy in the dimensions of image characteristics and detection performance with smaller parameters. Compared with the Inception V3 with similar parameter amount, this method improves the LCC index by 2.849% and the SRCC index by 3.338%.

Citation Download Citation

Fanshu Shen, Yan Wen, and Zhengrong Zuo "Assessment algorithm of scene complexity based on X-CENet", Proc. SPIE 12473, Second International Conference on Optics and Communication Technology (ICOCT 2022), 124731D (18 November 2022); https://doi.org/10.1117/12.2653877

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KEYWORDS

Convolution

Detection and tracking algorithms

Feature extraction

Infrared radiation

Infrared imaging

Image processing

Data modeling

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