Deep Learning Target Detection System for Sewage Treatment.

Area of Science:

• Computer Science
• Environmental Engineering
• Artificial Intelligence

Background:

• Object detection has transitioned from traditional methods to AI-driven approaches, primarily deep learning algorithms.
• Deep learning, utilizing neural networks and convolutional neural networks, is increasingly applied in complex detection tasks.
• Treated sewage analysis presents unique challenges for accurate object detection.

Purpose of the Study:

• To evaluate and compare the performance of neural network (NN) and convolutional neural network (CNN) algorithms for object detection in treated sewage.
• To develop and test a target detection system based on these deep learning algorithms.
• To assess the stability and accuracy of AI-based detection systems against traditional methods.

Main Methods:

• Study of neural network and convolutional neural network algorithms within the deep learning framework.
• Development of a target detection system integrating both NN and CNN algorithms.
• Experimental detection and comparison of treated sewage using the developed AI systems and traditional methods.

Main Results:

• The CNN-based target detection system demonstrated a stable recognition rate of approximately 70% for treated sewage.
• The NN-based target detection system exhibited less stability, with recognition rates fluctuating around 60%.
• CNNs provided a more consistent performance in identifying objects within treated sewage samples.

Conclusions:

• Convolutional neural networks offer a more reliable and stable approach for object detection in treated sewage compared to traditional neural networks.
• The developed CNN system shows promise for automated and accurate analysis of treated wastewater.
• Further research can explore optimizing CNN architectures for enhanced performance in environmental monitoring applications.