The proliferation of floating anthropogenic debris in rivers has emerged as a pressing environmental concern, exerting a detrimental influence on biodiversity, water quality, and human activities such as navigation and recreation. The present study proposes a novel methodological framework for the monitoring the aforementioned waste, utilising fixed, in-situ cameras. This study provides two key contributions: (i) the continuous quantification and monitoring of floating debris using deep learning and (ii) the identification of the most suitable deep learning model in terms of accuracy and inference speed under complex environmental conditions. These models are tested in a range of environmental conditions and learning configurations, including experiments on biases related to data leakage. Furthermore, a geometric model is implemented to estimate the actual size of detected objects from a 2D image. This model takes advantage of both intrinsic and extrinsic characteristics of the camera. The findings of this study underscore the significance of the dataset constitution protocol, particularly with respect to the integration of negative images and the consideration of temporal leakage. In conclusion, the feasibility of metric object estimation using projective geometry coupled with regression corrections is demonstrated. This approach paves the way for the development of robust, low-cost, automated monitoring systems for urban aquatic environments.

翻译：人为垃圾在城市河流中的泛滥已成为紧迫的环境问题，对生物多样性、水质以及航运和娱乐等人类活动产生不利影响。本研究提出了一种利用固定原位摄像机监测上述垃圾的新型方法框架。本研究做出两项关键贡献：(i) 利用深度学习对漂浮垃圾进行连续量化与监测；(ii) 在复杂环境条件下，从准确性和推理速度角度确定最合适的深度学习模型。这些模型在多种环境条件和学习配置下进行了测试，包括涉及数据泄露偏差的实验。此外，还实现了一个几何模型，用于从二维图像中估计检测目标的实际尺寸。该模型利用了相机的内参和外参特性。研究结果强调了数据集构建协议的重要性，特别是在整合负样本图像和考虑时间泄露方面。最后，证明了利用射影几何结合回归校正进行公制目标估计的可行性。该方法为开发面向城市水环境的鲁棒、低成本、自动化监测系统铺平了道路。