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

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