Reliable Multiple Object Detection on Noisy Images by Using Yolov3

Authors

  • VASANTHI PONDURI Department of Electronics and Communication Engineering, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Guntur, Andhra Pradesh, 522213, India
  • LAAVANYA MOHAN Department of Electronics and Communication Engineering, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Guntur, Andhra Pradesh, 522213, India

Keywords:

CNN, object detection, deep learning, YOLOV3, bounding box

Abstract

Object detection achieved very good performance by using deep learning models but there is a problem with noisy images. Due to the presence of noise in images, it is difficult to detect the object accurately. The main objective is to detect multiple objects in noisy images by using YOLOV3 approach. Generally, the CNN and R-CNN family algorithms extract the feature maps by using convolution operation with the striding method and objects detected by using neural network. But YOLOV3 algorithm directly applied on entire image and predict the bounding boxes along with labels and scores. In this article, the input noisy images smoothened by using median filter then YOLOV3 performs detection operation on entire image. Hence, YOLOV3 detects the object faster as compared with the other deep learning algorithms.

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Published

2023-05-20

How to Cite

PONDURI, V., & MOHAN, L. (2023). Reliable Multiple Object Detection on Noisy Images by Using Yolov3. International Journal of Communication and Computer Technologies, 9(1), 6–9. Retrieved from https://ijccts.org/index.php/pub/article/view/131

Issue

Section

Research Article