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In the object detection system, the detection algorithms separate the features of an image and classify it using some training algorithms. When the features feed to the trained algorithm, it can predict the features belongs to which category of object. There are several feature extraction techniques and training algorithms available to do object detection. Currently, the famous methods for object detection are using Machine learning and deep learning-based approaches.
There are many applications to object detection techniques. One of the famous applications is face detection and recognition. There are even commercial software products available in the market to do face recognition. Some of the main applications of object detection techniques are given in the following list.
The object detection algorithms using deep learning can be classified into two groups. The YOLO detector can predict the class of object, its bounding box, and the probability of the class of object in the bounding box. Each bounding box is having the following parameters. Each bounding box is associated with a probability value pc , it is the probability of a class of object in that bounding box. Each cell is responsible for predicting 5 bounding boxes there can be one or more objects in a cell.
If it does that, it will finally end up in bounding boxes for one image. Most of the bounding boxes in the cell may not have an object. The filtration of these bounding boxes is done based on the probability of object class pc. The non-max suppression processes will eliminate the unwanted bounding boxes and only the highest probability bounding boxes will remain.
Here are some of his web presence so that you can connect with him. This is one of his YouTube course on Computer Vision. There are very few implementations of the YOLO algorithm that exists on the web. Darknet is used as the framework for training YOLO, meaning it sets the architecture of the network. Given below the talk he has delivered at CVPR. It has 24 convolutional layers working for feature extractors and 2 dense layers for doing the predictions.
This architecture works upon is called Darknet. YOLO v2 Dec comes with some improvements from the first version. One of the shortcomings of the first version was, it is not very good at detecting objects that are near to the image and create mistakes in localization.
One of the new things introduced in YOLO v2 is anchor boxes. YOLO v3 released in April which adds further small improvements, included the fact that bounding boxes get predicted at different scales. In this version, the darknet framework expanded to 53 convolution layers. In Feb , Joseph Redmon announced he was leaving the field of computer vision due to concerns regarding the possible negative impact of his works. I stopped doing CV research because I saw the impact my work was having.
I loved the work but the military applications and privacy concerns eventually became impossible to ignore. Here I am assuming that you have a freshly installed Ubuntu It may break the existing packages. Use the following command to install the prerequisites, you can find the explanation of each one after that. CUDA is a parallel computing platform and application programming interface model created by Nvidia.
It allows software developers and software engineers to use a CUDA-enabled graphics processing unit for general purpose processing. OpenCV is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage then Itseez. The library is cross-platform and free for use under the open-source BSD license.
Otherwise, the output image from YOLO will be saved as an image file. You can install OpenCV in Ubuntu using the apt package manager or using compiling the source code. Installing OpenCV using package manager. Here is the command to quickly install OpenCV and its Python extension using the apt package manager. Installing OpenCV using source compilation. Install OpenCV in Ubuntu.
OpenMP uses a portable, scalable model that gives programmers a simple and flexible interface for developing parallel applications for platforms ranging from the standard desktop computer to the supercomputer. You can find the detailed video at the end of this post. Switch to the darknet folder after download. Open the Makefile in the darknet folder.
You can see some of the variables at the beginning of the Makefile. If you want to compile darknet for CPU, you can use the following flags. After doing these changes, just execute the following command from the darknet folder. You can build darknet using CMake build. Just follow the commands below in order to build from CMake.
Note : The commands should be executed from inside the darknet folder. After building those files, copy the darknet and libdark. You also have to rename. To test the darknet, first, we have to download a pre-trained model. After downloading the yolov4. Now make sure that you have the following files in the darknet folder. Now open a terminal from the darknet folder by right-clicking on the folder and execute the following commands.
The command below is for running YOLO in a single image. Both of the commands mentioned below do the same functions. Designed to be fast, easy to use and provide a minimal yet complete desktop environment, thanks to its own software repositories, always being updated to the […]. REMnux is a lightweight Linux distribution for assisting malware analysts in reverse-engineering malicious software.
The distribution is based on Ubuntu and is maintained by Lenny Zeltser. REMnux is designed for running services that are useful to emulate within an isolated laboratory environment when performing behavioral malware analysis. As part of this process, the analyst typically […]. After that it stopped development for quite some time, thankfully some new blood has picked it up and development has started again!
With over a year of inactivity, the latest alpha of nUbuntu 8. Since Ubuntu is getting so fantastically popular nowadays I thought this might be useful to some of you. I personally think Ubuntu is great, the features, ease of installation, stability and especially the work they have done on things like wireless drivers make it a breeze to get up and running.
It is a pretty […]. Sadly the author had to removed Java, Flash and Acrobat reader due to licensing agreements. Tag Archives ubuntu.
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