Abstract
Object detection, one of the most fundamental and challenging problems in computer vision. Nowadays some dedicated embedded systems have emerged as a powerful strategy for deliver high processing capabilities including the NVIDIA Jetson family. The aim of the present work is the recognition of objects in complex rural areas through an embedded system, as well as the verification of accuracy and processing time. For this purpose, a low power embedded Graphics Processing Unit (Jetson Nano) has been selected, which allows multiple neural networks to be run in simultaneous and a computer vision algorithm to be applied for image recognition. As well, the performance of these deep learning neural networks such as ssd-mobilenet v1 and v2, pednet, multiped and ssd-inception v2 has been tested. Moreover, it was found that the accuracy and processing time were in some cases improved when all the models suggested in the research were applied. The pednet network model provides a high performance in pedestrian recognition, however, the sdd-mobilenet v2 and ssd-inception v2 models are better at detecting other objects such as vehicles in complex scenarios.
Highlights
Computer vision systems have undergone a great development in the field of artificial intelligence in recent years
There are two important and distinct tasks to measure in object detection, these are: (a) to determine if an object is present and (b) where it is located within the image
The results of the process of detecting objects through different models of neural networks in the Jetson Nano NVIDIA are presented to support the findings of the research
Summary
Computer vision systems have undergone a great development in the field of artificial intelligence in recent years. In 2012, significant advances were made in image processing methods [9], one of which was the use of deep learning techniques. This has led to further research and application, the results of which have shown progress in the majority of computer vision challenges. There are many research activities that are conducted in the area of computer vision, one of the main ones is object detection, which aims to detect and position objects in the images such as traffic signs, vehicles, buildings, and people, to mention some. In contrast to the significant progress in object detection focusing on still images, video object detection has received less attention. Object detection for videos is realized by fusing the results of object detection on the current frame and object tracking from the previous frames
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