随着深度卷积神经网络优异的特征提取能力被发掘, 目标检测的进程开始以一种势不可挡的姿态向前推进, 同时, 和深度学习结合的目标检测技术取得了显著的成果, 在自动驾驶、智能化交通系统、无人机场景、军事目标检测和医学导航等现实场景中得到了广泛的应用. 本文回顾了传统目标检测算法的缺点, 介绍了常用的检测数据集以及性能评估指标, 综述了基于深度学习的目标检测经典算法, 阐述了当前目标检测的以及存在的困难与挑战, 对目标检测的未来可行的研究方向进行了展望.
With the exploration of the excellent feature extraction capabilities of deep convolutional neural networks, target detection has made a great stride. At the same time, the target detection technology combined with deep learning has achieved remarkable results. It has been widely used in such real scenarios as automatic driving, intelligent transportation systems, drone scenarios, military target detection, and medical navigation. The study reviews the shortcomings of traditional target detection algorithms and introduces commonly used detection data sets and performance evaluation indicators. It also summarizes classic target detection algorithms based on deep learning and elaborates on current target detection and existing difficulties and challenges. The feasible research directions in the future are prospected.