Description

Abstract:

Real-time object recognition -In this project, we will dive deeper and look at various algorithms that can be used for real-time object detection ( object recognition )using raspberry pi and OpenCV. We will start with the algorithms belonging to the RCNN family, i.e. RCNN, Fast RCNN, and Faster RCNN. In the upcoming article of this series, we will cover more advanced algorithms like Mobile net, SSD, etc. For this approach, the automatic classifier NN can be used for classification based on learning with some training samples of that category. This network uses the tangent sigmoid function as the kernel function. Finally, the simulated result shows that a used network classifier provides minimum error during training and better accuracy in classification. Real-time object recognition?

Real-time object recognition using Raspberry Pi and OpenCV

INTRODUCTION:

The algorithm applies a neural network to an entire image. The network divides the image into an S x S grid and comes up with bounding boxes, which are boxes drawn around images and predicted probabilities for each of these regions.

The method used to come up with these probabilities is logistic regression. The bounding boxes are weighted by the associated probabilities. For class prediction, independent logistic classifiers are used.

In this article, I am going to demonstrate how to implement the YOLO algorithm with a pre-trained model.

First, we would need to install DarkNet. DarkNet is a neural network framework that is open source. Real-time object recognition using Raspberry Pi and OpenCV

Real-time object recognition using Raspberry Pi and OpenCV

EXISTING SYSTEMS:

• Edge detection
• Morphological filters
• SVM classification

DISADVANTAGES:

• Not a real-time application
• Information about objects is very less
• The accuracy of output is less

Real-time object recognition using Raspberry Pi and OpenCV

PROPOSED SYSTEM:

• Caffe model data set(Darknet)
• Deep learning classification
• Blob detection

ADVANTAGES:

• Maximum accuracy in classification
• Real-time achievement
• Machine-based prediction
• Accuracy of output is increased

Real-time object recognition using Raspberry Pi and OpenCV

APPLICATIONS:

• Commercial applications
• Forensic lab
• Face recognition

BLOCK DIAGRAM:

HARDWARE BLOCK DIAGRAM:

CIRCUIT DIAGRAM:

Real-time object recognition using Raspberry Pi and OpenCV

HARDWARE REQUIREMENTS:

• Raspberry pi
• Camera

SOFTWARE REQUIREMENTS:

• Raspberry pi OS
• Python IDE
• OpenCV library

REFERENCE:

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• X. Wu, D. Hong, J. Tian, J. Chanussot, W. Li, and R. Tao, ??ORSIm detector: A novel object detection framework in optical remote sensing imagery using spatial-frequency channel features,?? IEEE Trans. Geosci. Remote Sens., vol. 57, no. 7, pp. 5146?5158, Jul. 2019.
•  S. Ren, K. He, R. Girshick, and J. Sun, ??Faster R-CNN: Towards real-time object detection with region proposal networks,?? IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137?1149, Jun. 2017.
• Y. Li, S. Li, C. Chen, A. Hao, and H. Qin, ??Accurate and robust video saliency detection via self-paced diffusion,?? IEEE Trans. Multimedia, vol. 22, no. 5, pp. 1153?1167, May 2020.
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• C. Chen, J. Song, C. Peng, G. Wang, and Y. Fang, ??A novel video salient object detection method via semi-supervised motion quality perception,?? 2020, arXiv:2008.02966. [Online]. Available: http://arxiv.org/abs/2008.02966
• Y. Li, S. Li, C. Chen, A. Hao, and H. Qin, ??A plug-and-play scheme to adapt image saliency deep model for video data,?? IEEE Trans. Circuits Syst. Video Technol., early access, Sep. 10, 2020, DOI: 10.1109/TCSVT.2020.3023080. [10] C. Mu, J. Liu, Y. Liu, and Y. Liu, ??Hyperspectral image classification is based on active learning and spectral-spatial feature fusion using spatial coordinates,?? IEEE Access, vol. 8, pp. 6768? 6781, 2020.
•  X. Wu, W. Li, D. Hong, J. Tian, R. Tao, and Q. Du, ??Vehicle detection of multi-source remote sensing data using an active fine-tuning network,?? ISPRS J. Photogramm. Remote Sens., vol. 167, pp. 39?53, Sep. 2020.