Abstract:

Object detection is one of the core tasks in computer vision, which is widely used in intelligent video monitoring, automatic monitoring, industrial detection and other fields. In recent years, with the rapid development of deep learning, the object detection algorithm based on deep convolutional neural network has gradually replaced the traditional object detection algorithm and become the mainstream algorithm in this field. Firstly, the common data sets and performance evaluation indexes of the object detection algorithm are introduced, and then the development of the convolutional neural network is introduced. Then, the two-stage object detection algorithm and the single-stage object detection algorithm are analyzed and compared. Finally, the future development of the object detection algorithm based on the deep convolutional neural network is prospected.

Key words: object detection, deep learning, computer vision, convolutional neural network

摘要：

目标检测是计算机视觉中的核心任务之一，在智能视频监控、自动化监测、工业检测等领域应用广泛。近些年来，随着深度学习的快速发展，基于深度卷积神经网络的目标检测算法逐渐替代了传统的目标检测算法，成为了该领域的主流算法。介绍了目标检测算法的常用数据集和性能评价指标，介绍了卷积神经网络的发展，重点分析比较了两阶段目标检测算法和单阶段目标检测算法，展望了基于深度卷积神经网络的目标检测算法未来的发展。

关键词: 目标检测, 深度学习, 计算机视觉, 卷积神经网络