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Attentive Layer Separation for Object Classification and Object Localization in Object Detection

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Abstract:

Object detection became one of the major fields in computer vision. In object detection, object classification and object localization tasks are conducted. Previous deep learning-based object detection networks perform with feature maps generated by completely shared networks. However, object classification focuses on the most discriminative object part of the feature map. Whereas, object localization requires a feature map that is focused on the entire area of the object. In this paper, we propose a novel object detection network by considering the difference between the two tasks. The proposed deep learning-based network mainly consists of two parts; 1) Attention network part where task-specific attention maps are generated, 2) Layer separation part where layers for estimating two tasks are separated. Comprehensive experimental results based on PASCAL VOC dataset and MS COCO dataset showed that proposed object detection network outperformed the state-of-the-art methods.

 

Introduction:

The algorithm applies a neural network to an entireimage. The network divides the image into an S x S gridand comes up with bounding boxes, which are boxesdrawn around images and predicted probabilities foreach of these regions.The method used to come up with these probabilities islogistic regression. The bounding boxes are weighted bythe associated probabilities. For class prediction,independent logistic classifiers are used.In this article, demonstrate how to implement the YOLOalgorithm with a pre trained model.First, we would need to install DarkNet. it is a neural

network framework that is open source.

 

 Existing Method:

  • Edge detection.
  • Morphological filters.
  • Svm classification.

 

 Disadvantages:

  • Not a real time application.
  • Information of objects is very less.

 

 Proposed Method:

  • Caffe model data set(Darknet)
  • Deep learning classification
  • Blob detection

 

 Advantages:

  • Maximum accuracy in classification
  • Real time achievement
  • Machine based prediction

 

 Applications:

  • Commercial applications
  • Forensic lab
  • Face recognition

 

 Block Diagram:

Attentive Layer Separation for Object Localization in Object Detection

 

 

Software Requirements:

  • Python idle.
  • Open CV modules.

 

 Hardware Requirements:

  • Windows OS PC.
  • Minimum 2GB RAM.
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  • Price
    Free
  • Instructor pantech team
  • Duration 15 Hrs
  • Enrolled 0 student
  • Access 3 Months

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