Exploring Riverine Litter Detection by Developing Comprehensive Dataset and Deep Learning

With the rapid growth of the economy, the problem of plastic pollution in rivers is becoming increasingly severe, particularly in key river basins such as the Taihu Basin. Plastic pollution not only disrupts aquatic ecosystems but also poses a threat to human health and regional economic development. Therefore, it is imperative to take effective measures to reduce plastic pollution in rivers in order to protect the environment and promote sustainable development. This study proposes an efficient river trash detection method by combining unmanned equipment and deep learning technology. A dataset comprising 1,347 RGB images of river trash, captured under diverse environmental conditions, was developed to offer a wealth of diversity for model training. YOLOv10-N is employed for object detection and an mAP@0.5 of 95% on the dataset is achieved. The research results highlight the potential of applying deep learning techniques in environmental monitoring and providing support for ecological protection. In addition, the contribution of this study's dataset provides valuable resources for future model training, with diverse types of images enhancing the model's generalization capabilities and offering possibilities for more effective litter collection.