Accurate and fast single shot multibox detector. Issue 6 (13th August 2020)

• Record Type: Journal Article
• Authors: Guo, Lie; Wang, Dongxing; Li, Linhui; Feng, Jindun
• Is Part Of: IET computer vision. Volume 14:Issue 6(2020)
• Page Start: 391
• Page End: 398
• Publication Date: 2020-08-13
• Subjects: image representation -- image segmentation -- object detection -- feature extraction -- learning (artificial intelligence) -- convolutional neural nets; fast single shot multibox detector -- deep learning -- object detection -- feature enhancement module -- fc_7 feature maps -- CCE module -- fine‐grained feature information -- FEM‐s module -- Nvidia 2080Ti GPU -- conv4_3 feature maps -- context information; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2019.0711
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : With the development of deep learning, the performance of object detection has made great progress. However, there are still some challenging problems, such as the detection accuracy of small objects and the efficiency of the detector. This study proposes an accurate and fast single shot multibox detector, which includes context comprehensive enhancement (CCE) module and feature enhancement module (FEM). To integrate more efficient information when aggregating context information, the conv4_3 and fc_7 feature maps are merged to design the CCE module. To obtain more fine‐grained feature information, this study presents a FEM and special feature enhancement module (FEM‐s) module that can fuse different receptive field sizes to better adapt to the scale change of the object. Compared to existing methods based on deep learning, the proposed method helps to gradually produce more detailed feature maps with better performance. Under the premise of ensuring real‐time speed, the authors network can achieve 81.2 mean average precision on the PASCAL VOC 2007 test with an input size of 320 × 320 on a single Nvidia 2080Ti GPU.