关键词: 深度学习, 目标检测, 迁移学习, 特征提取, 计算机视觉

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