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Emergency vehicle classification

Creation of ONNX classification model that loads in latest OpenCV and can effectively classify emergency vehicles from top front/top front side view (camera). Visual greyscale image of vehicle and audio processing of siren can be used.

Classification has to complete within 200ms on 500 GFLOPs.

Error rate:

0.97 positive for Emergency classification (1/30 emergency vehicles can be incorrectly classified as non-emergency).

0.99 positive for Non-emergency classification (1/100 of vehicles can be incorrectly classified as emergency).

You will have to create your own dataset but will also be provided RTSP access to a camera with frequent ambulance appearances.

This has to be done for ambulance and fire trucks marked according to specific standard (provided in attachment).

All data with source code needs to be handed over.

Happy to add any information that is required.

Kemahiran: Neural Networks, Sains Data, Machine Learning (ML)

Tentang Klien:
( 0 ulasan ) Krasnogorsk, Russian Federation

ID Projek: #33778368

8 pekerja bebas membida secara purata $10438 untuk pekerjaan ini

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tecogno

Hello, We at Tecogno Solutions are a team of Passionate Data Science and Full Stack professionals having more than five years of combined experience in multiple areas including Backend, Frontend, Machine learning (ML) Lagi

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FxDataLabs

Implementing projects using Python and Machine Learning is our core forte since we are working on it for more than 5+ years now. We are a team of 50+ developers who have successfully delivered 350+ Machine Learning Pro Lagi

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hayteekeys

Hello, I am an MSC majored in mathematics I have rich exp in ACM/ICPC and deep understandings of algorithms for Classifications, NLP, Image, speech and text Processing,Neural Networks,e.t.c I attended at ACM regional c Lagi

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