摘要
本文概述了计算机视觉技术在食品外观质量检测、成分分析与识别、微生物污染检测以及保质期预测等方面的具体应用,分析了其在食品检测中面临的挑战,并提出数据增强与迁移学习、精细化特征提取与背景抑制、轻量级模型设计与硬件加速、多模态信息融合与法规适应性设计等优化策略与技术创新.本研究旨在为提升食品检测的效率与准确性提供有力支持.
Abstract
This paper summarizes the specific applications of computer vision technology in food appearance quality inspection,component analysis and identification,microbial contamination detection and shelf life prediction,analyzes its challenges in food testing,and proposes optimization strategies and technological innovations such as data augmentation and transfer learning,refined feature extraction and background suppression,lightweight model design and hardware acceleration,multimodal information fusion and regulatory adaptability design.The purpose of this study is to provide strong support for improving the efficiency and accuracy of food testing.
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