Abstract:

Object detection is one of the most basic problems in the field of computer vision, which has been widely discussed and studied. In recent years, the development of deep convolution neural network has solved the problem of object detection better, and the detection accuracy has been greatly improved, but there are still many challenges in practical applications. Recent research methods are summarized from four aspects according to the current hot research trends in the field of object detection, aiming at different object detection challenges and problems, such as large range of object scale changes, real-time detection problems, weakly supervision detection problems, unbalanced samples, the relationship between different algorithms is  analyzed, the new improved methods, detection process and implementation effect are expounded. The detection accuracy, advantages, disadvantages and application scenarios of different algorithms are compared in detail. Finally, several possible directions for further development are discussed.

Key words: object detection, convolutional neural network, multifarious scale, real-time detection, weakly supervision, unbalanced samples

摘要：

目标检测是计算机视觉领域最基本的问题之一，已经被广泛地探讨和研究。虽然近年来基于深度卷积神经网络的目标检测方法使得检测精度有了很大提升，但是在实际应用中仍然存在较多挑战。综述了目标检测领域的最新研究趋势，针对不同的目标检测挑战和难题：目标尺度变化范围大、实时检测问题、弱监督检测问题和样本不均衡问题，从四个方面综述了最近的目标检测研究方法，分析了不同算法之间的关系，阐述了新的改进方法、检测过程和实现效果，并详细比较了不同算法的检测精度、优缺点和适用场景。最后讨论了未来有可能进一步发展的几个方向。

关键词: 目标检测, 卷积神经网络, 多尺度, 实时检测, 弱监督, 样本不均衡