基于深度学习的摄像头目标检测算法研究与优化

Research and Optimization of Camera Object Detection Algorithm Based on Deep Learning

为了解决现有目标检测算法在实际应用中存在的问题,文中提出了一种综合方法,包括数据预处理和标注、深度学习模型选择与构建、模型训练与调优以及模型评估与指标分析。另外,进一步探索了目标检测算法优化的方法。结果表明,目标检测算法在目标定位和分类精度方面表现出色,且具有较高的准确性和鲁棒性。

In order to solve the problems of existing object detection algorithms in practical applications,this paper proposes a comprehensive method,including data preprocessing and annotation,deep learning model selection and construction,model training and optimization,and model evaluation and indicator analysis.Further exploration was conducted on the optimization methods of object detection algorithms,and the results showed that the object detection algorithms performed excellently in target localization and classification accuracy,and had high accuracy and robustness.