Computer Vision Algorithms on a Raspberry Pi 4 for Automated Depalletizing

Abstract

The primary objective of a depalletizing system is to automate the process of detecting and locating specific variable-shaped objects on a pallet, allowing a robotic system to accurately unstack them. Although many solutions exist for the problem in industrial and manufacturing settings, the application to small-scale scenarios such as retail vending machines and small warehouses has not received much attention so far. This paper presents a comparative analysis of four different computer vision algorithms for the depalletizing task, implemented on a Raspberry Pi 4, a very popular single-board computer with low computer power suitable for the IoT and edge computing. The algorithms evaluated include the following: pattern matching, scale-invariant feature transform, Oriented FAST and Rotated BRIEF, and Haar cascade classifier. Each technique is described and their implementations are outlined. Their evaluation is performed on the task of box detection and localization in the test images to assess their suitability in a depalletizing system. The performance of the algorithms is given in terms of accuracy, robustness to variability, computational speed, detection sensitivity, and resource consumption. The results reveal the strengths and limitations of each algorithm, providing valuable insights for selecting the most appropriate technique based on the specific requirements of a depalletizing system.

Keywords

• depalletizing systems
• single-board computer
• Raspberry Pi 4
• computer vision
• object detection
• pattern matching