Volume 11 - Volume 11
Multiple Object Recognition Using OpenCV
Abstract
For automatic vision systems used in agriculture, the project presents object characteristics analysis using image processing techniques. In agriculture science, automatic object characteristics identification is important for monitoring vast areas of crops, and it detects signs of object characteristics as soon as it occurs on plant leaves. Image content characterization and supervised
classifier type neural network are used in the proposed deciding method. Pre-processing, image
segmentation, and detection are some of the image processing methods used in this form of decision
making. An image data will be rearranged and, if necessary, a region of interest will be selected
during preparation. For network training and classification, colour and texture features are
extracted from an input. Colour characteristics such as mean and variance in the HSV colour space,
as well as texture characteristics such as energy, contrast, homogeneity, and correlation. The device
will be trained to automatically identify test images in order to assess object characteristics. With
some training samples of that type, an automated classifier NN could be used for classification
supported learning in this method. The tangent sigmoid function is used as the kernel function in
this network. Finally, the simulated results show that the used network classifier has a low error
rate during training and higher classification accuracy. In the previous researches Object detection
has been made possible, but in our current research we have attempted to do live Object Detection
using OpenCV and also the techniques involved in it.
Paper Details
PaperID: 1795
Author's Name: D. Kavitha, B.P. Rishi Kiran, B. Niteesh and S. Praveen
Volume: Volume 11
Issues: Volume 11
Keywords: OpenCV, HSV, CNN, SVM Classifier.
Year: 2021
Month: April
Pages: 1736-1747