Deepthi Babu doing Aerospace Engineering in Karunya Institute of Technology and an intern at Object Automation Software Solutions, a leading data science solutions and services company at Chennai, India. She is familiar with a Hardware of drones and interested in adding new skills to her repertoire. She is proficient in Aerodynamic Stimulations and Gas Dynamics.
ABSTRACT: Forest fires are on the increase worldwide. Forest fires are a threat to our environment because they spread quickly and can burn down acres of lush forest if they are not attended to. Forest fires occur due to various reasons. As climate changes continue and temperatures increase by a few degrees every year, the forest fire will increase also. Trees that took many years to grow disappear in a very short time because of fires, leaving mountain areas barren, no longer providing protection from rains and mudslides following those rains, no longer providing oxygen clean air and shelter and food for birds and animals.
Usually the forest fires originate very discreetly and rangers are notified about the fire until it’s too late. This is because fire occur in dense forests where humans can’t possibly pose a challenge for the rangers. So, to overcome this problem we will use Drones to navigate into the thick part of the forest and integrate Computer Vision into this by utilizing a state-of-the-art Convolutional Neural Network (CNN) to achieve the task. The entire process is treated as classification task where the deep neural network model is responsible for classifying whether it's fire or non-fire from the image provided by the camera which is attached to the drone we deployed. The training is performed over a dataset containing both fire and non-fire images, collected from various sources.
KEYWORDS: Enterprise AI, POWER9, AC922, Deep Learning(DL), Neural Network.
PROPOSED SYSTEM: We will develop the proper neural network architecture for the problem based on the data and the goal set. Using a large set of data obtained from various resources and department of forestry, we will train the neural network to provide the most optimal strategy. Using the test data, we will test the neural network for its ability to provide the optimal strategy. We will use the forest fires of that year and have the domain experts to verify the optimality of the neural network’s strategy. Proposed models are very complicated, and require intrinsic knowledge about specific programming languages and tools. Setting up the system for DL model is difficult. Personal systems lack computational powers, which restricts the capabilities of DL models. Hence, a need for on Premise based DL servers are required to help a large mass of people. To meet this need, IBM has revealed Power9 processor, the AC922 Power systems server, designed for computing heavy artificial intelligence workloads.
To provide up to 5.6 times the bandwidth for data-intensive workloads, the AC922 Power server incorporates next-generation I/O architectures such as PCle Gen4, CAPI2.0, OpenCAPI and Nvidia NVLINK. We use this server to train our model and optimize our performance in an efficient way.