【摘要】 目标检测是计算机视觉领域的核心任务之一。随着深度学习的迅猛发展,基于深度学习的目标检测技术已经成为该领域的主流算法,被广泛应用于人脸检测、车辆检测、行人检测以及无人驾驶等领域。本文系统总结了当前基于深度学习的目标检测算法的研究进展,对各算法的优、缺点及其在VOC2007和COCO数据集上的检测结果进行了全面分析,并对基于深度学习的目标检测算法的未来发展进行了展望。更多还原

【Abstract】 Object detection is one of the core tasks in the field of computer vision. In recent years, with the rapid development of deep learning, the object detection technology based on deep learning has become the very popular mainstream algorithm. It has been widely used in many fields, such as face detection, vehicle detection, pedestrian detection, and unmanned driving, etc.. This paper systematically summarizes the current research progress of deep learning-based object detection algorithms, and thoroughly analyzes the advantages and disadvantages of each algorithm and its results on the datasets VOC2007 and COCO. Finally, the future development of object detection based on deep learning is also discussed in this paper.更多还原