A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection

Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan

Abstract:

We propose average Localisation-Recall-Precision (aLRP), a unified, bounded,
balanced and ranking-based loss function for both classification and localisation
tasks in object detection. aLRP extends the Localisation-Recall-Precision (LRP)
performance metric (Oksuz et al., 2018) inspired from how Average Precision (AP)
Loss extends precision to a ranking-based loss function for classification (Chen et
al., 2020). aLRP has the following distinct advantages: (i) aLRP is the first ranking based loss function for both classification and localisation tasks. (ii) Thanks to
using ranking for both tasks, aLRP naturally enforces high-quality localisation
for high-precision classification. (iii) aLRP provides provable balance between
positives and negatives. (iv) Compared to on average ∼6 hyperparameters in the
loss functions of state-of-the-art detectors, aLRP Loss has only one hyperparameter,
which we did not tune in practice. On the COCO dataset, aLRP Loss improves
its ranking-based predecessor, AP Loss, up to around 5 AP points, achieves 48.9
AP without test time augmentation and outperforms all one-stage detectors. Code
available at: https://github.com/kemaloksuz/aLRPLoss.