Abstract:

Object detection is an important research direction of computer vision, its purpose is to accurately identify the category and location of a specific target object in a given image. In recent years, the feature learning and transfer learning capabilities of deep convolutional neural networks have made significant progress in target detection algorithm feature extraction, image expression, classification and recognition. This paper introduces the research progress of target detection algorithm based on deep learning, the characteristics of common data sets and the key parameters of performance index evaluation, compares and analyzes the network structure and implementation mode of target detection algorithm formed by two-stage, single-stage and other improved algorithms. Finally, the application progress of the algorithm in the detection of human faces, salient targets, pedestrians, remote sensing images, medical images, and grain insects is described. Combined with the current problems and challenges, the future research directions are analyzed.

Key words: deep learning, object detection, transfer learning, feature extraction, computer vision

摘要：

目标检测是计算机视觉的一个重要研究方向，其目的是精确识别给定图像中特定目标物体的类别和位置。近年来，深度卷积神经网络（Deep Convolutional Neural Networks，DCNN）所具有的特征学习和迁移学习能力，在目标检测算法特征提取、图像表达、分类与识别等方面取得了显著进展。介绍了基于深度学习目标检测算法的研究进展、常用数据集特点以及性能指标评价的关键参数，对比分析了双阶段、单阶段以及其他改进算法的网络结构和实现方式。阐述了算法在人脸、显著目标、行人、遥感图像、医学图像、粮虫等检测领域的应用进展，结合当前存在的问题和挑战，展望分析了其未来的研究方向。

关键词: 深度学习, 目标检测, 迁移学习, 特征提取, 计算机视觉