ANALYSIS OF EFFECTIVE IMAGE PROCESSING METRICS ON RASPBERRY PI AND NVIDIA JETSON NANO

Abstract

Image processing and object recognition technologies for detecting defects on the surfaces of a variety of materials is critical to ensuring the safety and durability of infrastructure. That is why this topic and problems of the field of defect detection were chosen in the study. Therefore, the development of effective image processing methods for defect identification, especially in low- light conditions and hard-to-reach places, is of great relevance. The research is a comparison of classical image processing methods with modern deep learning algorithms such as CNN (Convo- lutional Neural Networks) and YOLO (You Only Look Once). The study analyzes the effective- ness of these methods under specific defect inspection conditions, including diffuse lighting and device mobility. An important aspect is the use of microcomputers such as Raspberry Pi and Nvidia Jetson Nano, which ensures the mobility and autonomy of the system. The practical value of the research lies in the implementation of effective image processing methods for detecting de- fects on the surfaces of engineering structures. This makes it possible to significantly improve the accuracy of surface defect identification, which is confirmed by the IoU (Intersection over Union) and Dice metrics. In particular, using CNNs for surface defect identification showed 35% better results compared to existing implementations of similar networks and 12% more efficient com- pared to YOLO. On the other hand, YOLO proved to be more productive in terms of processing frames per second on microcomputers, which is important for real-time monitoring. Keywords: computer vision, deep learning, image processing, surface defect detection, Rasp- berry PI, Nvidia Jetson Nano, performance metrics. Overview.