An end-to-end pollution analysis and detection system using artificial intelligence and object detection algorithms

Environmental pollution is generally a by-product of various human activities. Researchers have studied the dangers and harmful effects of pollutants and environmental pollution for centuries, and many necessary steps have been taken. Modern solutions are being constantly developed to tackle these issues efficiently. Visual pollution analysis and detection is a relatively less studied subject, even though it significantly impacts our daily lives. Building automatic pollution or pollutants detection systems has become increasingly popular due to the modern development of advanced artificial intelligence systems. Although some advances have been made, automated pollution detection is not well-researched or fully understood. This study demonstrates how various object detection models could identify such environmental pollutants and how end-to-end applications can analyze the findings. We trained our dataset on three popular object detection models, YOLOv5, Faster R-CNN (Region-based Convolutional Neural Network), and EfficientDet, and compared their performances. The best Mean Average Precision (mAP) score of 0.85 was achieved by the You Only Look Once (YOLOv5) model using its inbuilt augmentation techniques. Then we built a minimal Android application, using which volunteers or authorities could capture and send images along with their Global Positioning System (GPS) coordinates that might contain visual pollutants. These images and coordinates are stored in the cloud and later used by our local server. The local server utilizes the best-trained visual pollution detection model. It generates heat maps of particular locations, visualizing the condition of visual pollution based on the data stored in the cloud. Along with the heat map, our analysis system provides visual analytics like bar charts and pie charts to summarize a region's condition. In addition, we used Active Learning and Incremental Learning methods to utilize the newly collected dataset by building a semi-autonomous annotation and model upgrading system. This also addresses the data scarcity problem associated with further research on visual pollution.