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Mask R-CNN model implementation using scripts for filtering Singapore's public API information from real-time traffic.

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Geobm/Singapore-API-Mask-RCNN-model

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This repository uses Singapore's public API information from real-time traffic.

About this dataset

Returns links to images of live traffic conditions along expressways and Woodlands & Tuas Checkpoints.

Managed By Land Transport Authority
Last Updated February 13, 2018, 14:49 (SGT)
Created April 8, 2016, 15:55 (SGT)
Coverage From March 1, 2016
Frequency Real-time
Source(s) Land Transport Authority
Licence Singapore Open Data Licence

API Example value

{
  "api_info": {
    "status": "healthy"
  },
  "items": [
    {
      "timestamp": "2020-06-15T17:30:38.245Z",
      "cameras": [
        {
          "timestamp": "2020-06-15T17:30:38.245Z",
          "camera_id": 0,
          "image_id": 0,
          "image": "string",
          "image_metadata": {
            "height": 0,
            "width": 0,
            "md5": "string"
          }
        }
      ]
    }
  ]
}

Mask R-CNN Theory

Mask R-CNN is an instance segmentation technique which locates each pixel of every object in the image instead of the bounding boxes. It has two stages: region proposals and then classifying the proposals and generating bounding boxes and masks. It does so by using an additional fully convolutional network on top of a CNN based feature map with input as feature map and gives matrix with 1 on all locations where the pixel belongs to the object and 0 elsewhere as the output.

Backbone is a FPN style deep neural network. It consists of a bottom-up pathway , a top-bottom pathway and lateral connections. Bottom-up pathway can be any ConvNet, which extracts features from raw images. Top-bottom pathway generates feature pyramid map which is similar in size to bottom-up pathway. Lateral connections are convolution and adding operations between two corresponding levels of the two pathways. FPN outperforms other single ConvNets mainly for the reason that it maintains strong semantically features at various resolution scales.

Image segmentation

Partitioning a digital image into multiple segments (sets of pixels, also known as image objects). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. In particular, Mask R-CNN performs instance segmentation which means that different instances of the same type of object in the input image, for example, car, should be assigned distinct labels. This project is mainly focused on car and trucks image segmentation. Hence, to have a cuantitative measure of the traffic in Singapore expressways, Woodlands & Tuas Checkpoints before and during the global pandemic.

Mask R-CNN model

Architecture

Data Flow

Data FLow

FPN composes of a bottom-up and a top-down pathway. The bottom-up pathway is the usual convolutional network for feature extraction. As we go up, the spatial resolution decreases. With more high-level structures detected, the semantic value for each layer increases.

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Mask R-CNN model implementation using scripts for filtering Singapore's public API information from real-time traffic.

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