Undergraduate Graduation Design Practice in Computer Science and Technology

Abstract
This paper takes the undergraduate graduation design of the Computer Science and Technology major as the research object, and fully records the entire process of the project "Design and Implementation of a Rapid Goods Recognition System for Cabinets Based on Deep Learning" from initiation to implementation. It focuses on analyzing the key actions and core gains in the stages of topic selection and decision-making, technical preparation, engineering practice, and reflection and summary. In the topic selection stage, it breaks through the limitations of traditional development directions and finally selects the uncontacted deep learning field among small program development, conventional system design, and deep learning applications. In the technical preparation stage, it builds a theoretical framework for target detection and system development based on pre-graduate study during the winter vacation and literature review. In the engineering practice stage, aiming at the problem of insufficient computing power of personal equipment, it systematically explores server rental and selection schemes, and overcomes a series of technical difficulties in environment setup, code reproduction, and function integration. Finally, a cabinet goods recognition system with image detection, video recognition, and result export functions is completed. The research confirms that this graduation design not only realizes the cross-field integration of professional knowledge but also cultivates the abilities of independent learning, problem diagnosis, and engineering implementation. It provides a reusable graduation design practice paradigm for computer major students and helps them efficiently complete the core tasks in the final stage of their studies.
Keywords: Undergraduate Graduation; Deep Learning; Cabinet Goods Recognition; Server Configuration; YOLOv7 Model

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