OptimalNN: A Neural Network Architecture to Monitor Chemical Contamination in Cancer Alley

Abstract

The detrimental impact of toxic chemicals, gas, and oil spills in aquatic environments poses a severe threat to plants, animals, and human life. Regions such as Cancer Alley exemplify the profound consequences of inadequately controlled chemical spills, significantly affecting the local community. Given the far-reaching effects of these spills, it has become imperative to devise an efficient method for early monitoring, estimation, and cleanup, utilizing affordable and effective techniques. In this research, we explore the application of U-shaped neural Network (UNET) and U-shaped neural network transformer (UNETR) neural network models designed for the image segmentation of chemical and oil spills. Our models undergo training using the Commonwealth Scientific and Industrial Research Organization (CSIRO) dataset and the Oil Spill Detection dataset, employing a specialized filtering technique to enhance detection accuracy. We achieved training accuracies of 95.35% and 91% by applying UNET on the Oil Spill and the CSIRO datasets after 50 epochs of training, respectively. We also achieved a training accuracy of 75% by applying UNETR to the Oil Spill dataset. Additionally, we integrated mixed precision to expedite the model training process, thus maximizing data throughput. To further accelerate our implementation, we propose the utilization of the Field Programmable Gate Array (FPGA) architecture. The results obtained from our study demonstrate improvements in inference latency on FPGA.

Keywords

• artificial neural network
• transformers
• chemical contamination
• neuromorphic circuits
• cancer valley
• mixed-precision