目标检测的损失函数研究进展
Research Progress of Loss Function in Object Detection

DOI: 10.12677/CSA.2021.1111288, PP. 2836-2844

Keywords: 目标检测，损失函数，交并比，L_n范数
Object Detection, Loss Function, Intersection over Union, L_n Norm

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Abstract:

损失函数是目标检测中的一个研究热点。在深度学习飞速发展的过程中，目标检测算法取得众多研究成果，并广泛应用于人脸检测、门禁识别、自动驾驶等领域。为了进一步了解目标检测的发展细节，本文从目标检测的损失函数优化出发，梳理概括近年来目标检测中分类损失和回归损失的发展进程，并对其进行归类、分析。首先介绍分类损失的发展历程，然后介绍回归损失的两大发展方向：基于L_n范数与基于交并比(IoU)的损失函数，并分析这些损失函数的优缺点以及相互之间的关联性。最后，对基于目标检测的损失函数的未来发展方向进行展望。
Loss function is a research hotspot in object detection. With the rapid development of deep learning, object detection algorithms have achieved many research results, and are widely used in face detection, access control recognition, and automatic driving and so forth. In order to further understand the development details of object detection, this article starts from the optimization of the loss function of object detection, combs and summarizes the development process of classification loss and regression loss in object detection in recent years, and classifies and analyzes them. First introduce the development history of classification loss, and then introduce the two major development directions of regression loss: loss function based on L_n norm and based on Intersection over Union (IoU), and analyze the advantages and disadvantages of these loss functions and the correlation between them. Finally, the future development direction of the loss function based on object detection is prospected.

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