LRP metriğinin doğrudan en iyilenmesi ile nesne tespitçilerinin eğitilmesi

This thesis focuses on training deep object detection networks by directly optimizing the localisation-recall-precision (LRP) performance metric that can evaluate classification and localisation performance of an object detector in a unified manner (Oksuz et al., 2018). To achieve this goal, unlike the commonly used linear weighting approach, we aim to implicitly optimize the LRP metric first by using a bounded localisation loss from previous works and proposing a loss function that can bound the range of classification task loss. In addition to this range balancing approach, we aim to train an object detector with an LRP regressor trained with LRP values collected during the training stage. We show that the proposed regression architecture can estimate LRP values with low error rates. However, training an object detector by attaching the regressor architecture as a differentiable LRP error estimator did not yield satisfactory results. Finally, by adapting the perceptron learning algorithm based approach proposed by Chen et al. (2020), we show that we can embed the LRP metric as a loss function to train a deep object detector. In this thesis, this perceptron learning-based approach is examined, and its generalization to all IoU based localisation loss functions is proposed.