# Computer Vision Algorithms on a Raspberry Pi 4 for Automated Depalletizing
## 5 Conclusions  
This article presented the implementation of four computer vision algorithms, namely pattern matching, scale-invariant feature transform (SIFT), Oriented FAST and Rotated BRIEF (ORB), and Haar cascade classifier [1; 2; 3; 4; 5; 6; 7; 8], on a single hardware platform with low computing power, consisting of a Raspberry Pi 4, and evaluates their performance on the task of box recognition and localization to assess their suitability in a depalletizing system. Each technique has been described in detail and the respective implementations have been outlined. The experimental results were analyzed, evaluating the performance of the algorithms in terms of accuracy, robustness to variability, computer speed, sensitivity of detection, and resource consumption.  
The results showed that pattern matching achieved high accuracy in object detection with a simple configuration process. SIFT demonstrated robustness to variations in scale, rotation, and illumination, contributing to its versatility in various applications. ORB showed limitations in detecting certain object orientations, but remained reliable under certain conditions. Despite a long training process, the Haar cascade classifier showed effective detection performance and acceptable accuracy.  
The choice of the most appropriate technique depends on the specific requirements of the particular depalletizing system, considering factors such as accuracy, robustness, computer speed, and resource constraints. Each method has shown strengths and limitations, and the optimal choice may vary based on the unique characteristics of the application.  
Future research in this area could significantly benefit from exploring the integration of multiple computer vision techniques. By leveraging their respective strengths and mitigating their limitations, we can create more robust and efficient systems. \"Accuracy\" and \"diversity\" are two relevant keywords in this context [38]. For example, an integration of the pattern matching, SIFT, and Haar classifier models studied in this paper could be promising, each having low latency, good detection capability, and being very different from each other. It is also worth noting that the detection latency of the combined model would be no more than 1 s, which is entirely compatible with the time constants of the application at hand.  
Additionally, as already discussed in Section 2.3, investigating advanced deep learning-based object detection methods and their applicability to depalletizing systems could further enhance the system's performance and robustness even when not running on a Raspberry.  
Overall, this research contributes to the advancement of automated object detection and localization in industrial environments, paving the way for more efficient and reliable depalletizing processes, ultimately enhancing productivity and operational excellence in factory settings.  
D.G.: conceptualization, supervision, methodology, writing, drafting of the article, review and editing, M.F. and A.V.R.: software implementation, experiments and data curation; F.M., S.R. and A.C.: conceptualization, visualization, supervision, review. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data available on request from the authors.  
The authors declares no conflict of interest.