The loss function is a crucial factor that affects the detection precision in the object detection task. In this paper, we optimize both two loss functions for classification and localization simultaneously. Firstly, we reconstruct the classification loss function by combining the prediction results of localization, aiming to establish the correlation between localization and classification subnetworks. Compared to the existing studies, in which the correlation is only established among the positive samples and applied to improve the localization accuracy of predicted boxes, this paper utilizes the correlation to define the hard negative samples and then puts emphasis on the classification of them. Thus the whole misclassified rate for negative samples can be reduced. Besides, a novel localization loss named MIoU is proposed by incorporating a Mahalanobis distance between the predicted box and target box, eliminating the gradients inconsistency problem in the DIoU loss, further improving the localization accuracy. Finally, the proposed methods are applied to train the networks for nighttime vehicle detection. Experimental results show that the detection accuracy can be outstandingly improved with our proposed loss functions without hurting the detection speed.

损失函数是影响目标检测任务中检测精度的关键因素。在本文中，我们同时优化了分类和定位的两个损失函数。首先，我们结合定位的预测结果重建分类损失函数，旨在建立定位和分类子网络之间的相关性。与现有研究中仅在正样本之间建立相关性并应用于提高预测框定位精度的研究相比，本文利用相关性来定义硬负样本，然后将重点放在它们的分类上。因此可以降低负样本的整体误分类率。除了，通过在预测框和目标框之间加入马氏距离，提出了一种名为 MIoU 的新型定位损失，消除了 DIoU 损失中的梯度不一致问题，进一步提高了定位精度。最后，将所提出的方法应用于训练用于夜间车辆检测的网络。实验结果表明，在不影响检测速度的情况下，我们提出的损失函数可以显着提高检测精度。