Multiple Object Recogntion using OpenCV and Python

Description

ABSTRACT:

The project presents object characteristics analysis using image processing techniques for automated vision systems used in the agricultural field.  In agriculture research of automatic object characteristics detection is essential in monitoring large fields of crops, and thus automatically detects symptoms of object characteristics as soon as they appear on plant leaves. The proposed decision-making system utilizes image content characterization and a supervised classifier type of neural network. Image processing techniques for this kind of decision analysis involve preprocessing feature extraction and a classification stage. At Processing, an input image will be resized and region of interest selection performed if needed. Here, color and texture features are extracted from the input for network training and classification. Color features like mean, the standard deviation of HSV color space, and texture features like energy, contrast, homogeneity, and correlation. The system will be used to classify the test images automatically to decide object characteristics. For this approach, the automatic classifier NN is used for classification based on learning with some training samples of that same category. This network uses the tangent sigmoid function as the kernel function. Finally, the simulated result shows that the used network classifier provides minimum error during training and better accuracy in classification.

SYSTEM ANALYSIS

EXISTING SYSTEM 

• SUPPORT VECTOR MACHINE

• K-MEANS CLUSTERING

• RANDOM  tree classifier

PROPOSED SYSTEM 

• PREPROCESSING
• RGB COLOR MODEL 
• GLCM FEATURE EXTRACTION
• BLOB DETECTION
•  Artificial NEURAL NETWORK

Advantage

• Maximum accuracy in classification
• Real-time achievement
• Machine-based prediction

REQUIREMENT ANALYSIS

HARDWARE REQUIREMENT

• MONITOR 
• HDD: 1TB
• RAM: 8GB

SOFTWARE REQUIREMENT

PYTHON 3.9 VERSION

BLOCK DIAGRAM

Reference

[1] X. Wu, D. Hong, J. Chanussot, Y. Xu, R. Tao, and Y. Wang, ‘‘Fourier-based rotation-invariant feature boosting: An efficient framework for geospatial object detection,’’ IEEE Geosci. Remote Sens. Lett., vol. 17, no. 2, pp. 302–306, Feb. 2020. 

[2] 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.

 [3] 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. 

[4] 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.

 [5] K. Kang, W. Ouyang, H. Li, and X. Wang, ‘‘Object detection from video tubeless with convolutional neural networks,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CPR), Jun. 2016, pp. 817–825.