SFGNet detecting objects via spatial fine-grained feature and enhanced RPN with spatial context. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Hu, Jun; Wang, Yongfeng; Cheng, Shuai; Liu, Jiaxin; Kang, Jiawen; Yang, Wenxing
• Is Part Of: Systems science & control engineering. Volume 10:Issue 1(2022)
• Page Start: 388
• Page End: 406
• Publication Date: 2022-12-31
• Subjects: Convolutional neural networks -- small-size object detection -- spatial fine-grained features -- online hard example mining; System theory -- Periodicals; Automatic control -- Periodicals; 003.05
• DOI: 10.1080/21642583.2022.2062479
• Languages: English
• ISSNs: 2164-2583

Abstract

Abstract : Object detection, which is one of the most fundamental visual recognition tasks, has been a hotspot in computer vision. CNN (Convolutional Neural Networks) have been widely employed for building detector. Due to the success of RPN (Region Proposal Network), the two-stage detectors get both classification accuracy and precise regression bounding boxes. However, they still struggle in small-size object detection. In this paper, we present a deep network, namely Spatial Fine-Grained Network (SFGN). The SFGN that exploits Spatial Fine-Grained Features (SFGF) concatenates the higher resolution features, which is fine-grained with the low resolution features and high-level semantic by stacking spatial features for fine-grained features. An enhanced region proposal generator is proposed to get the objectless for small object to obtain a small set of proposal. The contextual information surrounding the region of interest is embedded using local spatial information for increasing the useful information and discriminating the background. For improving the detection performance, we use a simple yet surprisingly effective online hard example mining (OHEM) algorithm for training region proposal generator. It embeds an efficiently implemented soft non-maximum suppression (soft-NMS) for replacing with tradition NMS to obtain consistent improvements without increasing the computational complexity in inference. On PASCAL VOC 2007 and PASCAL VOC 2012 datasets, our SFGN improves baseline model from 81.2% mAP to 80.6% mAP. On MS COCO dataset, SFGN also performs better than baseline model. As intuition suggests, our detection results provide strong evidence that our SFGN improves detection accuracy, especially in small object test.