Application of Image Enhancement and Object Detection Technologies in Virtual Teaching Systems for Vocational Skills Training

With the advancement of digital transformation, vocational skills training is increasingly shifting towards virtual teaching models.However, image quality in such systems often varies due to limitations in device performance and environmental conditions, while object detection faces challenges related to diverse object morphologies and sensitivity to lighting conditions.These issues necessitate the development of efficient image enhancement and object detection techniques to improve teaching effectiveness.Traditional image enhancement methods show limited performance in complex instructional scenarios, and deep learning-based general enhancement models often fail to adapt to the specific object features and learner needs in vocational training contexts.Similarly, existing object detection algorithms struggle with accuracy and real-time performance due to the morphological and lighting diversity of objects in virtual teaching images.To address these challenges, this study focuses on virtual teaching systems for vocational skills training and conducts research in two key areas: (1) enhancing image quality by improving image enhancement networks based on instructional image characteristics, and developing a parameter prediction network to enable personalized enhancement; (2) optimizing the structure and parameters of object detection models based on the YOLOv8 algorithm to improve detection accuracy and real-time performance in complex scenes.The research outcomes provide high-quality image inputs and accurate object detection for virtual teaching systems, supporting the development of intelligent interaction and automated assessment functions.This contributes both theoretically and practically to the digital and intelligent transformation of vocational skills training.