Machine Learning Examples

Question:

Remove the watermark on images.Images vary on different sizes, Preferably You may use deep learning

Machine and Deep learning to detect cracks on the road surface from 20M pixel images and report them geospacially. The defects to be detected are dependent on the road type. There are three basic road types:- 1. Bituminous, 2. Concrete and 3. Bituminous overlaid concrete. CRACKING All road types can have cracking. This can be longitudinal (along the road) or transverse (across the road) or random. The random cracking can form crazed areas with many cracks.

The software needs to detect all forms of cracking and be able to show the detected ones as an overlay on the image. Images should be stored - overlaid image and cracks only image. The full image should not be analysed. An investigation area should be specified. The next stage is reporting and quantifying the results. Transverse cracks are the simplest and chainage in metres and coordinates(centre position) need recorded. Longitudinal cracks need a start and end chainage and…

Problem Statement:

Implement Object Detection from video Stream with the help of cctv footage or camera in our restaurant and detect a specific object and then call our web API upon seeing that object in Camera.

To develop a machine learning model for predicting crop based on the weather variables using KNN.  

Dataset attached:

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