We are working on an Automated Foreign Object Detection on Runway (FYP Project). It is a system to detect the FODs (Foreign Object Debris) on the surface of Runways. It also detects other anomalies like wildlife, snow, ice pavement, cracks, etc. in all weather conditions (like fog/smog, rain, dark weather etc.) Cameras will be mounted on poles at the sides of Runways to detect them and report to the Airport staff in Real-Time.
I am not sure what technique will be best for its implementation as I am new to this field. I am currently researching about Keras, YOLO, DNN, R-CNN and others. I want your opinion on how should we implement it,
Thank you.
Before considering which ML implementation to choose I would suggest that you consider how are you going to construct the feature vector and what kind of preprocessing are you going to use. Each of the objectives above present different challenges and different degrees of difficulty (identification under different lighting, visibility, movement vs stationary objects, etc.) and I would highly recommend treating them separately on different image processing/ML systems
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