# Root Dilaceration Using Deep Learning:
## 2 Materials and Methods
### Object Detection and Detectors
This model can augment the spatial sampling locations in the modules with additional offsets and learn the offsets from the target tasks, without additional supervision. DetetoRS is a deep learning model created by [PERSON] et al. [33]. It provides a new mechanism in the backbone design. This model uses Recursive Feature Pyramid and Switchable Atrous Convolution. ResNet50 was used as backbone. Dynamic R-CNN is a two-stage object detection model [34]. It addresses dynamic training procedures to mitigate inconsistency problems between the fixed settings by adjusting the shape of regression loss function and the label assignment criteria automatically. ResNet50 was used as a backbone. NAS-FPN is a deep learning model developed by [PERSON] et al. [35]. It proposes a new feature pyramid architecture to overcome the large search space of pyramidal architectures. This model uses a combination of scalable search space and a neural architecture search algorithm instead of manually designing architectures for pyramidal representations. Grid R-CNN is a novel object detection model which adopts a grid-guided localization mechanism for accurate object detection [36]. It gives better average precision on the COCO benchmark compared to Faster R-CNN with a ResNet50 backbone and FPN architecture. Resnext101 was used as a backbone. The Hybrid Task Cascade (HTC) model was created by modifying the Cascade Mask R-CNN model designed by [PERSON] et al. [37]. It takes advantage of a powerful cascade architecture. Faster RCNN with weight standardization was presented by [PERSON] et. al. [38]. FreeAnchor is an approach that updates the hand-crafted anchor assignment to free anchor matching by formulating detector training as a maximum likelihood estimation (MLE) procedure [39]. It allows objects to match anchors in a flexible manner. ResNet50 was used as a backbone. FCOS (Fully Convolutional One-Stage Object Detector) is an object detection model created by [PERSON] et al. [40]. Most state-of-the-art object detectors rely on pre-defined anchor boxes, but this model is anchor-box free, as well as proposal free. The elimination of anchor boxes eliminates complicated computations. ResNet50 was used as a backbone. Adaptive Training Sample Selection (ATSS) is a method of automatically selecting positive and negative samples based on the statistical properties of the object [41]. This method fills the gap between anchor-based (Faster R-CNN, YOLOv3 etc.) and anchor-free detection (FCOS, FoveaBox etc.). ResNet101 was used as a backbone. FoveaBox, like FCOS, is a completely anchor-free object detection model [42]. This model directly learns the object's existing possibility and the bounding box coordinates without an anchor reference by predicting category-sensitive semantic maps for the object's existing possibility. Side-Aware Boundary Localization (SABL) is an approach developed by [PERSON] et al. [43]. It proposes a two-step localization scheme, which first predicts a range of movement through bucket prediction and then pinpoints the precise position within the predicted bucket. We used a transfer learning concept in this work. Detectors that were pre-trained on the COCO (Common Objects in Context) dataset were employed. Transfer learning refers to leveraging feature representations from pre-trained models. Pre-trained models were usually trained on a sufficient amount of data that was a standard benchmark for computer vision applications. It allows the use of the weights of the pre-trained models while initializing the weights for a new application, dental images in our case. Practically, transferring information from previously learned tasks for the learning of new tasks has the potential to significantly improve performance.