CUD a-accelerated fast training of Locally connected Neural Pyramid using YIQ color coding

Kurhade, Anirudha Shrikant; Thakare, Akash; Phadke, Anuradha C.

doi:10.1109/iadcc.2014.6779482

Cited by 1 publication

(1 citation statement)

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“…They have proposed an automatic Chestnut Selection system which works on the features like the oval shape for detecting the chestnut product and colour and size for defect detection. Aniruddha et al [13] implemented a Locally-connected Neural Pyramid (LCNP) using the CUDA platform for recognition of objects from disparate classes like a person, car, building, etc. In this work, they achieved speedy training of large datasets and a recognition rate of 85.62% for the testing samples.…”

Section: B Literature Surveymentioning

confidence: 99%

Automated Crop Field Surveillance Using Computer Vision

Khare

Phadke

2020

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

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Artificial Intelligence is everywhere today. But unfortunately, Agriculture has not been able to get that much attention from Artificial Intelligence (AI). A lack of automation persists in the agriculture industry. For over many years, farmers and crop field owners have been facing a problem of trespassing of wild animals for which no feasible solution has been provided. Installing a fence or barrier like structure is neither feasible nor efficient due to the large areas covered by the fields. Also, if the landowner can afford to build a wall or barrier, government policies for building walls are often very irksome. The paper intends to give a simple intelligible solution to the problem with Automated Crop Field Surveillance using Computer Vision. The solution will significantly reduce the cost of crops destroyed annually and completely automate the security of the field.

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Section: B Literature Surveymentioning

confidence: 99%