Real-Time River Plastic and Debris Detection System using Computer Vision

The pollution of the riverine plastics is now viewed as one of the most significant environmental hazards since the rivers represent one of the largest paths of plastic waste transportation between the land and the ocean. The traditional methods of surveillance, such as manual survey and field inspection, are labour intensive, one consuming both time and unable to provide any form of large-scale surveillance. To address these weaknesses, the present paper will propose an automated system, based on vision, to detect and analyze floating debris under river conditions using the approach of deep learning. The proposed remedy entails the application of video images of fixed surveillance cameras to detect and classify floating waste using the assistance of the object detection models constructed in accordance with the convolutional neural networks. The system works with image frames, which detect the objects of debris such as the plastic bottles and bags among other floating debris and converts the detects into a measurable data such as the quantity of the debris and its density. In addition, the outputs of detection can be analyzed through time and then one would be able to identify the trends of pollution and the potential locations of the hotspots. The proposed structure will provide a cost-effective, efficient and scalable monitoring system of the river pollution. The system will help environmental monitoring bodies make decisions based on data on waste management, planning of rivers cleanup, and policy of pollution control by automating the process of recognizing and analyzing the violations. The proposed solution demonstrates the way the computer vision and deep learning technologies may improve the effectiveness and quality of the river pollution monitoring systems.