Improved single shot multibox detector target detection method based on deep feature fusion. (16th September 2021)

• Record Type: Journal Article
• Authors: Bai, Dongxu; Sun, Ying; Tao, Bo; Tong, Xiliang; Xu, Manman; Jiang, Guozhang; Chen, Baojia; Cao, Yongcheng; Sun, Nannan; Li, Zeshen
• Is Part Of: Concurrency and computation. Volume 34:Number 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-16
• Subjects: deep feature fusion -- machine learning -- SSD -- target detection; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6614
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The feature layers of different layers in the single shot multibox detector (SSD) are independently used as the input of the classification network, so it is easy to detect the same object. This article proposes an improved SSD model based on deep feature fusion. In the SSD algorithm, the deep feature fusion between the target detection layer and its adjacent feature layer is used, including convolution kernels and pooling kernels of different sizes, down‐sampling of low‐level features and up‐sampling of deconvolution of high‐level features. The network is improved by combining the target frame recommendation strategy in the SSD algorithm and the frame regression algorithm. The experimental results show that the improved SSD algorithm improves the detection accuracy and detection rate of the target, and the effect is more obvious for the relatively small‐scale target.