## 2 Developed Methodology
### Typology Detector  
To estimate the pose of the tool head based on its digital model, the correct digital model must be selected first. To expedite this selection, we trained a classification model that can recognize the different tool heads and return a classification probability for each tool head based on the video feed from the camera. These probabilities are then used to display a list of tool heads in the user interface (UI), which is sorted from most to least likely. This means that the user can simply select the first entry in the list for a correctly classified tool head or one of the first couple of tool heads in the list if the model is uncertain. Since the tool heads have complex shapes and the background is uncontrolled, we decided to deploy a deep learning system and compared two of the standard architectures for image classification: ResNet18 [40] and EfficientNetV2S [41]. The two architectures were chosen over more recent larger models, because the inference has to run on the NUC, which has limited computational resources available. Both models were trained on a total of 10,879 images (see Figure 5). The dataset encompass images of nine different tool heads obtained through two distinct processes: first, images deliberately collected for training purposes. This involved mounting the camera on a specific tool and moving it to mimic the period preceding a carpenter's commencement of the subsequent fabrication step. Second, additional images were extracted from videos recorded during test fabrications and manually classified. Notably, to prevent any leak of information between the validation and training datasets, the training and validation data were extracted from separate sets of videos in both scenarios. The preprocessing procedure commences by resizing the images to dimensions 384 \(\times\) 384 and 256 \(\times\) 256 using bi-linear interpolation for the EfficientNet and ResNet18, respectively. Subsequently, for ResNet18, a central crop to size 224 \(\times\) 224 is applied. The pixel values are then rescaled to a range of 0-1. Following this, a channel-wise normalization is performed by subtracting the mean and dividing by the standard deviation. These statistical parameters are derived from the ImageNet1K v1 dataset [42], with means (R: 0.485, G: 0.456, B: 0.406) and standard deviations (R: 0.229, G: 0.224, B: 0.225). To increase  
Figure 4: (**a**) In the absence of production CAD models, tool heads can be easily digitized via the structure-from-motion (SfM) technique. (**b**) Nevertheless, raw captured point clouds need to be postprocessed via third-party software. (**c**) Finally, only the tool head needs to be isolated and meshed in a watertight fashion. the variability of our training data and make the model more robust, we augmented our data with random rotations, horizontal and vertical flips, and hue jittering. Because of the comparatively small number of training images, we decided to employ transfer learning instead of training the networks from scratch. The weights were initialized with the weights provided by torchvision and trained on the ImageNet-1K v1 dataset [42]. We replaced the classification head with a custom one to match the number of tools and froze all of the weights, except for the classification head, while training. ResNet18 was trained for 28 epochs until convergence with a batch size of 90, while EfficientNet took only 10 epochs to converge with a batch size of 90. To assess the effectiveness of the classifiers, we determined their overall classification accuracy. This was achieved by calculating the ratio of accurately classified images within our validation dataset to the entire count of images in the same dataset. Our findings revealed that EfficientNet significantly surpassed ResNet18, achieving a 93% accuracy rate compared to ResNet18's 43%.