Title: SSD object detection algorithm based on knowledge map

Authors: Li Huang; Xiaofeng Wang; Jianhua Lu; Wei Hu; Changrong Zhang

Addresses: College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei, China; China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan, Hubei, China ' College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei, China; China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan, Hubei, China ' College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei, China; China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan, Hubei, China ' College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei, China; China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology of the Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei, China; Precision Manufacturing Research Institute, Research Centre for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, Hubei, China

Abstract: With the pervasive integration of artificial intelligence into all aspects of human life, talent emerges as a primary resource. Upon analysing the current state of talent training in higher education institutions, issues such as dispersed knowledge points, overlapping content, a singular practice approach and ineffective evaluation have been identified. In response to these challenges, this paper proposes a multidisciplinary and comprehensive practical teaching methodology grounded in the knowledge graph framework. It delves into diverse paths for practical teaching and assessment, including aspects like teaching objectives, problem decomposition, resource integration, implementation methods and performance evaluation. The practical application of the SSD algorithm in researching service robot indoor object detection serves as an illustrative example. Employing the holistic practical teaching approach facilitated by the knowledge graph, this model guides students in acquiring object detection expertise, thereby enhancing their comprehensive development.

Keywords: knowledge map; integrated practice; SSD object detection algorithm.

DOI: 10.1504/IJWMC.2024.140274

International Journal of Wireless and Mobile Computing, 2024 Vol.27 No.2, pp.152 - 160

Received: 19 Sep 2023
Accepted: 10 Jan 2024
Published online: 01 Aug 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article