Abstract
Due to the rapid technological advancements in recent years, humans have gained the ability to design and implement the knowledge into machines and it also allows them to perform various functions such as autonomous thinking ability, understanding skills, and problem-solving. Moreover, machine learning [ML] plays an important role in developing the image-processing models and application. In real-time applications, the labels of objects may be unfamiliar to those who are unaware of them, or there may be several identical objects but are labeled differently. In this paper, the approach that would be useful to detect and classify various objects is presented with a trial study by utilizing various datasets. This method can be used to improve accuracy in finding the classification of similar objects. In the process of classifying real-time objects, this system uses a supervised learning technique, where various datasets are trained and compared with the queried object. Here, the Support Vector Machine (SVM) algorithm is utilized for performing analysis and decision making in object classification domain. The result of this project is to read the given objects with the help of computer vision and allowing the machine to perform the prediction or classification for the given object. More than 800 images obtained from standard datasets of trained labeled classes are implemented in our application to classify the objects.
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Harikrishna, J., Rupa, C., Gireesh, R. (2022). Deep Learning-Based Real-Time Object Classification and Recognition Using Supervised Learning Approach. In: Shakya, S., Balas, V.E., Kamolphiwong, S., Du, KL. (eds) Sentimental Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1408. Springer, Singapore. https://doi.org/10.1007/978-981-16-5157-1_10
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DOI: https://doi.org/10.1007/978-981-16-5157-1_10
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