Object detection using convolutional networks with adaptively adjusting receptive field of convolutional filter. Issue 6 (10th July 2019)

• Record Type: Journal Article
• Authors: Lu, Qishuo; Jiang, Zhuqing; Men, Aidong; Tang, Pengliang
• Is Part Of: IET computer vision. Volume 13:Issue 6(2019)
• Page Start: 562
• Page End: 568
• Publication Date: 2019-07-10
• Subjects: object detection -- image representation -- neural nets -- learning (artificial intelligence) -- convolution -- image classification; receptive field size -- convolutional filter -- named adaptive convolution -- convolutional neural networks -- adaptive convolutional networks -- deep convolutional network -- object detection task -- high level convolutional layers encode semantic features; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2018.5601
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : The receptive field size of a convolutional filter in a deep convolutional network is a crucial issue for object detection task, as the output must response to a suitable size of area in the image to capture proper information. Receptive field size of convolutional filter is fixed due to the inherently fixed geometric structure in its building module. However, objects of interest vary significantly in size within the images for object detection. Different locations of images correspond to objects with different scales, and high level convolutional layers encode semantic features over spatial positions, thus adaptive determination of receptive field size of convolutional filter is desirable for object detection. The authors propose a new module to adaptively determine the receptive field size of convolutional filter, named adaptive convolution. It is based on the idea of dilating the convolutional filter with multiple dilation values and choosing the maximum activation as output, without adding any other parameters. The plain counterparts in existing convolutional neural networks can be easily replaced by adaptive convolution, giving rise to adaptive convolutional networks. Adequate experiments have proven the effectiveness of authors' method.