# Object Detection for Industrial Applications: Training Strategies for AI-Based Depalletizer
## 1 Introduction
The information regarding the algorithm for the pose estimation was not treated. [PERSON] et al. [23] (2021) used a 2D camera to classify and locate pharmaceutical products. Using the 2D camera, the depalletizer was not able to detect the size of objects; for this reason, the authors improved the depalletizer using the 3D camera. The 3D camera was the Intel Realsense depth camera D435 with a 1920 \(\times\) 1080 resolution for RGB and a 1280 \(\times\) 720 resolution for the depth sensor. Both solutions used YOLOv3 as the CNN model to detect the object. The 3D pose estimation was not treated. [PERSON] et al. [24] (2022) used RGB-D data to classify and locate several objects. The architecture of their pose estimator was composed by a segmentation module, used to obtain the mask of the object in order to crop the 2D image and the point clouds. Then, the point clouds were managed by PointNet, which extracted the geometry features, while the 2D image was sent to a CNN to perform the edge extraction. The outputs of the PointNet and CNN were used in the Dense Fusion block to produce the 3D pose estimation. All the recent review papers about automatic vision-based depalletization systems propose a two-step approach, which consists of detecting the object within a 2D image and then elaborating the portion of the point cloud extracted by using the 2D RoI to estimate the pose of the specific object that has to be manipulated by a robot. Such a choice is mainly motivated by the high performance of the deep CNN, along with the advantages of transfer learning, in detecting different kinds of objects within a 2D image. Leveraging this aspect, most of the authors used a CNN pretrained on popular datasets, e.g., ImageNet and COCO,to fine-tune custom object detectors, mainly focusing on the final performance. However, to the best of authors' knowledge, the authors in this field did not focus on two main aspects that are important when a company has to develop a customized system for a new client, which are (a) the number of images of the custom setup that have to be acquired to train the CNN model and (b) the specific transfer learning approach that will lead to better performance. Compared with the traditional feature-based approaches, it has been proven in many application fields that the deep neural networks require a big amount of training data [25; 26; 27; 28; 29; 30]. Collecting big datasets in industrial environments is usually a time-consuming activity, which implies a substantial increase of the commissioning time. Regarding the transfer learning procedure, a possible approach might consider fine-tuning a network pretrained on datasets such as ImageNet or COCO with the new images collected by the system; however, an alternative method could contemplate adding another step of fine-tuning in the middle by using images of similar stock-keeping units, which, for example, could have been acquired with the same sensor. Envisaging an automatic vision-based depalletization system featuring the two processing steps described above and able to manipulate paper boxes, in this work, the authors studied and compared a set of possible approaches that could be followed to reach the desired performances on a custom AI-based object detection model for depalletization considering a variable training set size. The model takes as the input 2D images of a pallet loaded with paper boxes, and it is in charge of detecting all the boxes that have to be reached and grasped by the picking robot. By using different in-house datasets of paper boxes lying on a pallet, three different training approaches were considered: (1) the first regards the fine-tuning of a model pretrained on the COCO Dataset by using the images of the new paper boxes to be detected; (2) the second considers the training of a model pretrained on the COCO Dataset by using only images of paper boxes that are different from the new paper boxes; (3) the third one regards the fine-tuning of the model already fine-tuned at the previous point with images of the new paper boxes.