基于深度学习的目标检测模型综述
湖南工业大学, 计算机学院, 湖南株洲 412007
| 摘 要： | 目标检测通过在图像或视频中对物体出现的具体位置进行准确高效的定位以识别出待检测的实例，是计算机视觉领域中需要解决的经典问题。其应用范围广泛，包括人脸检测、智能驾驶辅助、卫星遥感检测等重要领域。本综述旨在帮助研究人员快速了解目标检测，从目标检测算法的概念出发，对目标检测算法的发展经历进行分析论述，详细地阐述了目标检测算法从独自发展到与深度学习技术相结合的进化历程。本文根据目标检测由检测过程是否产生锚点框进行划分，并从基于锚点框、无锚点框类型出发，分析了单阶段、两阶段的目标检测算法的研究现状，详细归纳了目标检测算法发展历程中的经典模型结构，并介绍了目标检测中的难点，即解决在训练过程中不带有详细标注的少样本情况下目标检测问题的思路。最后，本文总结对比各种经典模型的优缺点，对主流数据集及评价指标等进行全面归纳，并对目标检测领域现今所面临的挑战和未来的发展方向进行了展望。 |
| 关 键 词： | 计算机视觉; 深度学习; 目标检测 |
| DOI： | 10.57237/j.cst.2023.02.006 |
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收稿日期: 2023-03-02
接受日期: 2023-04-12
在线出版日期: 2023-04-25
A Survey of Object Detection Models Based on Deep Learning
College of Computer Science, Hunan University of Technology, Zhuzhou 412007, China
| Abstract: | Object detection is a crucial problem in computer vision that involves accurately locating objects in images or videos to identify instances to be detected. It has various applications, including facial detection, intelligent driving assistance, and satellite remote sensing detection. This review aims to aid researchers in quickly comprehending object detection. It covers the concept of object detection algorithms, analyzes the development history of object detection algorithms, and elaborates on the evolution process of object detection algorithms from independent development to combination with deep learning technology. The article divides object detection based on whether anchor boxes are generated during the detection process. It discusses the types of anchor boxes and non-anchor boxes and analyzes the current research status of single-stage and two-stage object detection algorithms. It also summarizes classic model structures in the development process of object detection algorithms and introduces difficulties in object detection. The article aims to solve the problem of object detection with a small number of samples and without detailed annotations during the training process. It summarizes and compares the advantages and disadvantages of various classic models, mainstream datasets, and evaluation indicators. Additionally, it looks forward to current challenges and future development directions in the field of object detection. |
| Keywords: | Computer Vision; Deep Learning; Object Detection |
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