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Accurate Object Detection with YOLO
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| Author:
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J. AVANEESH, J. THANGAKUMAR, T. SUDALAIMUTHU, P. RANJANA, N. SATYA PRAKASH, N.V.V. SAI TEJA
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| Abstract:
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There are many algorithms used for object detection, the algorithms use classifiers for detecting objects which consumes a lot of time. Yolo is used for the object detection it uses bounding boxes and class probabilities to detect the images. A single neural network is formed and is used for detection. The whole process takes single evaluation. Since it is done in single pass, we can optimize using end-to-end interactions. YOLO algorithm is pretty faster and accurate compared to other algorithms. In real time, YOLO processes image at 45 frames per second. Whereas we can adopt faster results at 155 frames per second which reduces the accuracy level but still holds double of the maP (mean average precision) of other real time detectors.
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| Keyword:
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Artificial Intelligence, CNN, Detection, Object, YOLO.
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| EOI:
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-
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| DOI:
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https://doi.org/10.31838/ijpr/2020.12.01.235
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| Download:
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