论文标题
对象检测的优化损失功能：关于夜间车辆检测的案例研究
Optimized Loss Functions for Object detection: A Case Study on Nighttime Vehicle Detection
论文作者
论文摘要
损耗函数是影响对象检测任务中检测精度的关键因素。在本文中，我们同时优化了两个分类和本地化的两个损失函数。首先，通过将基于IO的系数乘以分类损失函数中的标准跨熵损失，可以建立定位和分类之间的相关性。与现有的研究相比，仅应用相关性以提高阳性样品的定位准确性，本文利用相关性获得了真正硬的负样本，并旨在降低负样本的错误分类速率。此外，提出了一种新的定位损失，该损失是通过在预测的框和目标框之间纳入Mahalanobis距离，从而消除了DIOU损失中的梯度不一致问题，从而进一步提高了定位准确性。最后，在两个数据集上进行了足够的夜间车辆检测实验。我们的结果表明，与提出损失功能的火车相比，检测性能可以得到证实。源代码和训练有素的模型可在https://github.com/therebellll/negiou-posiou-miou上找到。
Loss functions is a crucial factor that affecting the detection precision in object detection task. In this paper, we optimize both two loss functions for classification and localization simultaneously. Firstly, by multiplying an IoU-based coefficient by the standard cross entropy loss in classification loss function, the correlation between localization and classification is established. Compared to the existing studies, in which the correlation is only applied to improve the localization accuracy for positive samples, this paper utilizes the correlation to obtain the really hard negative samples and aims to decrease the misclassified rate for negative samples. Besides, a novel localization loss named MIoU is proposed by incorporating a Mahalanobis distance between predicted box and target box, which eliminate the gradients inconsistency problem in the DIoU loss, further improving the localization accuracy. Finally, sufficient experiments for nighttime vehicle detection have been done on two datasets. Our results show than when train with the proposed loss functions, the detection performance can be outstandingly improved. The source code and trained models are available at https://github.com/therebellll/NegIoU-PosIoU-Miou.
