Overview
This study introduces a methodological framework for monitoring floating anthropogenic debris in urban rivers. The system utilizes fixed, in-situ cameras, deep learning techniques, and a geometric model to quantify and track debris. The research addresses the environmental concern of floating debris, which impacts biodiversity, water quality, and human activities such as navigation and recreation.
Research Context
The proliferation of floating anthropogenic debris in rivers constitutes an environmental concern. The study's approach aims to provide continuous quantification and monitoring of this debris, which can have detrimental effects on aquatic ecosystems and human interactions with river systems.
Approach
The proposed framework integrates deep learning for continuous quantification and monitoring of floating debris. A primary objective was to identify the most suitable deep learning model, considering both accuracy and inference speed, particularly under complex environmental conditions. These models underwent testing across a range of environmental conditions and learning configurations, including specific experiments addressing potential biases related to data leakage.
In addition to deep learning, a geometric model was implemented to estimate the actual size of detected objects. This model derives size estimations from 2D images by leveraging both the intrinsic and extrinsic characteristics of the camera used for image acquisition. The methodology explores the integration of negative images into the dataset constitution protocol and considers the impact of temporal leakage.
Findings
- The study demonstrated the feasibility of continuous quantification and monitoring of floating debris using deep learning.
- Optimal deep learning models were identified based on their accuracy and inference speed, even when operating in complex environmental conditions.
- Experiments revealed the significance of the dataset constitution protocol, particularly regarding the integration of negative images.
- The consideration of temporal leakage was identified as an important factor in the experimental setup.
- The research confirmed the feasibility of metric object estimation using projective geometry, complemented by regression corrections. This geometric model utilizes both intrinsic and extrinsic camera characteristics to estimate actual object sizes from 2D images.
Why This Matters
The demonstrated approach allows for the development of robust, low-cost, automated monitoring systems tailored for urban aquatic environments. Such systems can contribute to addressing the pressing environmental concerns associated with floating anthropogenic debris.