SLMS-SSD: Improving the balance of semantic and spatial information in object detection. (15th November 2022)

• Record Type: Journal Article
• Authors: Wang, Kunfeng; Wang, Yadong; Zhang, Shuqin; Tian, Yonglin; Li, Dazi
• Is Part Of: Expert systems with applications. Volume 206(2022)
• Publication Date: 2022-11-15
• Subjects: Object detection -- Deep learning -- Multi-scale feature selection -- Self-learning feature fusion; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117682
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: With the development of deep learning technology, the research on convolutional neural network-based object detection is becoming more and more mature. However, most methods are unsatisfactory in dealing with the issue of semantic and spatial information imbalance. In this article, we extend the single-shot multibox detector SSD and propose a self-learning multi-scale object detection network by balancing the semantic information and spatial information, named SLMS-SSD. We first construct a shallow feature enhancement module to enhance the representation of small objects by extracting richer context information. Second, in terms of feature connectivity, we design a multi-scale feature selection module for intermediate layer features with a combination of top-down and direct up-samplings. Finally, in terms of feature strength, we design a self-learning feature fusion module for measuring the feature importance. We validate our model on the PASCAL VOC and MS COCO datasets, and the results demonstrate that it can effectively improve the accuracy of object detection, especially the accuracy of small object detection. Highlights: Designing feature connectivity to balance semantic and spatial information. Self-learning feature strength to measure the importance of different features. Extracting shallow feature contextual information that facilitates detection. Improving accuracy compared to SSD series methods, especially for small objects.