  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 $\sim$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 .
