(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 15, No. 3, 2024 273 | Page www.ijacsa.thesai.org Design and Implementation of a Real-Time Image Processing System Based on Sobel Edge Detection using Model-based Design Methods Taoufik Saidani 1 *, Refka Ghodhbani 2 , Mohamed Ben Ammar 3 , Marouan Kouki 4 , Mohammad H Algarni 5 , Yahia Said 6 , Amani Kachoukh 7 , Amjad A. Alsuwaylimi 8 , Albia Maqbool 9 , Eman H. Abd-Elkawy 10 Department of Computer Sciences-Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia 1, 2, 9, 10 Department of Information Systems-Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia 3, 4, 7 Department of Computer Science, Al-Baha University, Saudi Arabia 5 Department of Electrical Engineering-College of Engineering, Northern Border University, Saudi Arabia 6 Department of Information Technology-Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia 8 Abstract—Image processing and computer vision applications often use the Sobel edge detection technique in order to discover corners in input photographs. This is done in order to improve accuracy and efficiency. For the great majority of today's image processing applications, real-time implementation of image processing techniques like Sobel edge detection in hardware devices like field-programmable gate arrays (FPGAs) is required. Sobel edge detection is only one example. The use of FPGAs makes it feasible to have a quicker algorithmic throughput, which is required in order to match real-time speeds or in circumstances when it is critical to have faster data rates. The results of this study allowed for the Sobel edge detection approach to be applied in a manner that was not only speedy but also space-efficient. For the purpose of actually putting the recommended implementation into action, a one-of-a-kind high- level synthesis (HLS) design approach for intermediate data nodes that is based on application-specific bit widths was used. The high-level simulation code known as register transfer level (RTL) was generated by using the MATLAB HDL coder for HLS. The code written in hardware description language (HDL) that was produced was implemented on a Xilinx ZedBoard with the aid of the Vivado software, and it was tested in real time with the assistance of an input video stream. Keywords—Image processing; sobel edge detection; high level synthesis; model based design; Zynq7000 MATLAB HDL coder I. INTRODUCTION The evolving requirements and technological advancements have led to a growing demand for real-time image processing systems, widely utilized across several industries. Real-time embedded system designs prioritize completing tasks within a certain timeframe overachieving high speed. These systems are commonly utilized in driving support systems, driverless vehicles, flight control, security, and military systems. Predictability characteristics and time stability are essential requirements for real-time systems. [1]. The majority of contemporary image processing and computer vision systems still struggle with the basic challenge of identifying the area of interest in a picture. This is necessary for a wide range of applications, including advanced driving assistance systems (ADAS), which identify things like pedestrians, traffic signals, and blind spots; lane departure warning systems; video surveillance applications; and simultaneous localization and mapping (SLAM) [1]. One example of this kind of feature in a picture is a corner, which is the place at where two distinct edges meet. Image corner detection often involves the employment of many algorithms. The Moravec method [2], the Susan algorithm, and the Sobel edge detector are just a few examples of the kinds of corner extraction techniques that see regular application. The Sobel edge detector is one of the corner detecting algorithms that has the highest level of accuracy. The method is quite computationally demanding, despite the fact that its operation is remarkably simple. It is frequently utilized in systems that demand data processing in real time; as a result, traditional CPUs are unable to fulfill the requirements of these systems. CPUs are only useful when there are big amounts of data involved or when we need to execute calculations using floating point numbers. As a result, field-programmable gate arrays, often known as FPGAs, are great candidates for implementing such algorithms in real time as a result of their rapid processing rates and parallel implementations. It's possible that corner detection will need to be developed on the FPGA in addition to the other algorithms if you're going to be using it for sophisticated computations. Some examples are non-maxima suppression, matrix computation, and triangulation [3]. Other examples include matching by utilizing the sum of absolute differences. As a result of this prerequisite, it is essential to enhance the effectiveness as well as the space needs of the FPGA implementations for these algorithms [4]. A large number of academics have recently released original work on innovative implementations of the Efficient implementation of Sobel edge detection on FPGAs in terms of both space and time. Liu and colleagues proposed a method capable of processing RGB565 video at 640x480 resolution with a frame rate of 154 frames per second. Liu and colleagues