摘 要
随着智能制造技术的快速发展，机器视觉系统作为智能装备的核心组成部分，在工业检测、自动化装配等领域发挥着关键作用。然而，由于复杂环境干扰、图像噪声以及算法局限性等因素，现有机器视觉系统的精度仍难以满足高精度任务需求。为此，本研究以提升智能装备中机器视觉系统的精度为目标，深入探讨了影响精度的关键因素，并提出了一种基于深度学习与多传感器融合的优化方法。通过构建改进的卷积神经网络模型，结合多源数据校正算法，有效提升了目标识别和定位的准确性。同时，引入自适应参数调整机制，增强了系统在动态环境中的鲁棒性。实验结果表明，该方法能够显著降低误差率，提高检测精度约20%，并在多种实际应用场景中表现出优异的稳定性和适应性。
关键词：机器视觉系统 深度学习 多传感器融合
Abstract
With the rapid development of intelligent manufacturing technology, machine vision system, as a core component of intelligent equipment, plays a key role in industrial testing, automatic assembly and other fields. However, due to the complex environmental interference, image noise, and algorithm limitations, the accuracy of the existing machine vision systems is still difficult to meet the requirements of high-precision tasks. Therefore, with the goal of improving the precision of the machine vision system in intelligent equipment, this paper explores the key factors affecting the precision, and proposes an optimization method based on the fusion of deep learning and multiple sensors. By constructing an improved convolutional neural network model and combining with the multi-source data correction algorithm, the accuracy of target identification and positioning is effectively improved. Meanwhile, the adaptive parameter adjustment mechanism is introduced to enhance the robustness of the system in the dynamic environment. Experimental results show that the proposed method can significantly reduce the error rate, improve the detection accuracy by about 20%, and show excellent stability and adaptability in various practical application scenarios.
Keyword:Machine Vision System Deep Learning Multi-Sensor Fusion
目 录
1绪论 1
1.1智能装备中机器视觉系统的研究背景 1
1.2机器视觉精度优化的意义与价值 1
1.3国内外研究现状分析 1
1.4本文研究方法与技术路线 2
2机器视觉系统精度影响因素分析 2
2.1光学成像系统的误差来源 2
2.2图像采集设备的性能限制 3
2.3环境干扰对精度的影响 3
2.4数据处理算法的局限性 3
2.5精度优化的关键挑战 4
3机器视觉系统精度优化方法研究 4
3.1基于硬件改进的精度提升策略 4
3.2高效图像校正算法的设计 5
3.3数据增强技术的应用研究 5
3.4深度学习在精度优化中的作用 6
3.5多传感器融合的精度提升方案 6
4精度优化的实际应用与验证 7
4.1工业检测中的精度优化案例 7
4.2自动化装配中的应用效果分析 7
4.3质量控制中的精度提升实践 8
4.4实验结果与数据分析 8
4.5应用中存在的问题与改进建议 8
结论 9
参考文献 10
致谢 11
