# Object Detection for Industrial Applications: Training Strategies for AI-Based Depalletizer
## 1 Introduction  
The current state of robotics and Artificial Intelligence (AI) allows for the replacement of repetitive labor with automation, resulting in significant growth opportunities for efficiency and cost. As the worldwide economy recovers post-pandemic, the demand for labor has outstripped supply, in particular in strongly growing fields such as logistics and fulfillment [1]. In these sectors, the need to create an efficient and flexible supply chain, capable of satisfying the needs of the individual citizen, has been highlighted. Automation in the handling and storage of goods plays a fundamental role in optimizing the supply chain; those activities include repetitive and time-consuming tasks that do not add value to the finished product. Consequently, automating these tasks [2, 3] through the usage of advanced robotic solutions [4, 5, 6] can streamline the entire supply chain and provide a better-quality service to the citizens. In addition, this would allow operators to dedicate themselves to more complex tasks that are less subject to physiologically unfavorable postures. Amongthe set of available technologies, the interest around automated systems for depalletization is constantly increasing. This is due to the fact that depalletization is a very monotonous, strenuous, and sometimes, quite dangerous task [7,8]. Automated systems for depalletization are in charge of picking stock-keeping units from a pallet and placing them in an handling system, such as a conveyor. The first depalletization systems were based on fixed positions in space; thus, they required being programmed for each specific pallet layout. Since industrial scenarios are becoming more and more unstructured, currently, these systems are becoming obsolete as they do not provide a sufficient level of flexibility. Consequently, innovative depalletization systems based on computer vision and artificial intelligence are growing in popularity, since they are able to complete the task while requiring the least amount of information. In the last few years, many vision-based depalletizing techniques have been developed and proposed by the research community. The traditional approaches considered the processing of both 2.5D or 3D data of the scene by means of standard techniques mainly based on the extraction of geometric keypoints and 3D vertices or edges with the aim to solve an object matching problem for 3D pose estimation [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]. In recent years, new approaches based on deep convolutional neural networks have been proposed and investigated. In 2018, [PERSON] et al. [20] presented a solution that combined three depth streams into one common frame. [PERSON] et al. used a convolutional neural network, based on the DenseCap network, to perform the object detection of a 2D image. They used deep learning techniques to train their model, but the large number of parameters and the training set size to learn the feature were limitations. Using transfer learning, they solved this problems. In 2020, [PERSON] et al. [21] developed a flexible robotic depalletizing system for supermarkets, which used the Intel Realsense RGB-D camera to acquire the scene with the pallet. Supported by transfer learning, they trained a CNN model to recognize food boxes in the RGB image. They used the depth information to perform the pose estimation. [PERSON] et al. [22] (2021) used a vision system composed by a Sick Visionary-S depth camera, which was mounted at a 1.80 m height from the conveyor belt and observed the parcel boxes directly from above. Such a depth camera combines structure light patterns and stereo vision in order to acquire RGB image and 3D point clouds. The Mask R-CNN was used to detect the parcel boxes within the 2D image, which are objects without texture and always have an orthogonal planar face. The bounding boxes of the detected boxes were used to select a region of interest in the point clouds, and such a selected RoI was used to estimate the pose of the objects.