A combination between research lines of robotics and artificial intelligence and using computer vision, this consists of using robotic systems that can recognize and understand images and scenes, generally integrate the Areas: detection of objects image recognition and Image Generation Object detection is sophisticated and more in robotics due to countless applications that can develop through image processing. This article shows the implementation of the NVIDIA® Jetson development card in a remote control unmanned aerial system (RPAS) for object recognition, based on focal loss. Hence, it is a challenge to obtain results, which it faces to develop it will show in the expected final solution.
Technology development has allowed each day we have devices that can contain all the functionality of a Digital System on a single chip (SoC) and they have a very high scale of integration (VLSI) hundreds of millions of gates at very low costs. As well as the design, verification and synthesis tools offered by the development factories those make these SoC and FPGA components. This companies offer Integrated Development Environments with software tools to perform from the specification of the Design to its synthesis in C.I. and its verification in industry standard languages as Verilog and VHDL. This paper shows the advantages in design, verification, synthesis and testing that can be obtained by using HDL languages such as CHISEL, MyHDL for the processing of video processing in Real-Times and demonstrate its main advantages in both learning time and costs.
In recent years, applications in the processing of images have increased, in which the objective is to improve their quality (of the image and a correct human interpretation or to facilitate the search for information in the processed image. Motivation to use, the use of new technologies to solve computational problems with high-performance computing systems that have become indispensable, when it is required to have a response in a shorter time of the result.
At present, there are several systems among them, computer clusters and hybrid CPU-GPU systems that each have different characteristics and capabilities that can be applied to multiple problems. Image processing is an area that requires solving applications in a shorter time (real time), which is applicable in various areas of scientific research. The high-performance systems take advantage of all the computational processing resources using tools that measure the time of uses of the core, in which the cores are specially designed to perform matrix operations that run in parallel, this is by the number of nuclei that have the computer system. Also, you can choose the distribution of code and execution in this system many-core: the one, how, and where, the program will be executed in the many-core.
 ADAPTEVA. The Parallella Board, User´s Guide. URL: http://www.adapteva.com/parallella-board/
 BERNSTEIN A. J. Analysis for programs for parallel processing. Electronic Computer, IEEE Transactions on
Electronic Computers, Vol. EC-15, no. 5, pp. 757-763, Oct,1966.
 LAMPORT L.. How to make a multiprocessor computer that correctly executes multiporcess programs.
Computers, IEEE Transactions on Computers, Vol. C-28, no.9, pp. 690-691, Sept. 1979.
 SARITA V. ADVE AND HANS-J. BOEHM. Memory Models: A Case for Rethinking Parallel Languagues and
Hardware. Communications of the ACM, Vol. 53, no. 8, pp. 90–101, Aug, 2010.
 HU WIE WU AND XIA PEISU. Out-of-order Execution in Sequentially Consistent Shared-Memory System:
Theory and Experiments. Jornal of Computer. Science and Technology, Vol.13, no.2, Mar. 1998.