Abstract
The area of computer vision is emerging continually with the increasing interaction and development to provide a comfortable interaction between human and machines. One of the key aspects in the process of computer vision is object detection. Either objects can be identified partially or close to the original objects. The accuracy in detecting the objects can be improved by using state-of-the-art deep learning models like faster-Regional Convoluted Neural Network (faster-RCNN), You Only Look Once model (YOLO), Single Shot Detector (SSD) etc. Traditional algorithms can’t recognize objects as efficiently due to its limitations. Whereas the deep learning models require large amount of data for training the dataset, which has more resource and labour intensive in nature. The selection of algorithm determines its precision in object detection as well as its reliability. The recognition and classification of object begins with preparing dataset followed by splitting the dataset into training dataset and test dataset. The task of training the dataset can be assisted by both traditional as well as modern deep neural networks. The loss per step or epoch is calculated on the training dataset to signify the efficiency and accuracy of the model. In this model, the loss per step is 2.73. We have achieved a maximum accuracy of about 85.18% after training the dataset used.
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Howal, S., Jadhav, A., Arthshi, C., Nalavade, S., Shinde, S. (2020). Object Detection for Autonomous Vehicle Using TensorFlow. In: Pandian, A., Ntalianis, K., Palanisamy, R. (eds) Intelligent Computing, Information and Control Systems. ICICCS 2019. Advances in Intelligent Systems and Computing, vol 1039. Springer, Cham. https://doi.org/10.1007/978-3-030-30465-2_11
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