从传统方法到深度学习，系统梳理目标检测演进路径。
From classical techniques to convolution-based models: A review of object detection algorithms
- 按技术路线分两类：手工特征的传统方法与卷积神经网络模型
- 深度学习显著提升检测性能，实现自动提取多层次特征
- 适合计算机视觉研究者了解算法发展脉络
目标检测是计算机视觉与图像理解中的基础任务，旨在识别图像中感兴趣对象并定位其位置，同时赋予对应类别标签。传统方法依赖人工设计特征和浅层模型，在处理复杂视觉数据时表现有限，难以捕捉高层语义信息。深度学习，尤其是卷积神经网络（CNN），通过直接从数据中自动学习丰富、分层的特征，克服了上述局限，实现了语义与高层表征的有效提取。本文回顾目标检测框架的发展历程，将方法分为两大类：(1) 经典计算机视觉技术；(2) 基于CNN的检测器。对比分析主流CNN模型的优劣，并总结其在性能上的显著提升。最后指出未来研究的关键方向，以进一步推动目标检测性能优化。
原文摘要 · Abstract (English)
Object detection is a fundamental task in computer vision and image understanding, with the goal of identifying and localizing objects of interest within an image while assigning them corresponding class labels. Traditional methods, which relied on handcrafted features and shallow models, struggled with complex visual data and showed limited performance. These methods combined low-level features with contextual information and lacked the ability to capture high-level semantics. Deep learning, especially Convolutional Neural Networks (CNNs), addressed these limitations by automatically learning rich, hierarchical features directly from data. These features include both semantic and high-level representations essential for accurate object detection. This paper reviews object detection frameworks, starting with classical computer vision methods. We categorize object detection approaches into two groups: (1) classical computer vision techniques and (2) CNN-based detectors. We compare major CNN models, discussing their strengths and limitations. In conclusion, this review highlights the significant advancements in object detection through deep learning and identifies key areas for further research to improve performance.
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