YOLOv5n++: An edge-based improved YOLOv5n model to detect river floating debris.

Rivers serve as vital water sources, maintain ecological equilibrium, and enhance landscapes. However, the looming issue of floating debris stemming from improper waste disposal and illegal discharge, poses an imminent threat to river ecosystems and their aesthetic appeal. Conventional human-led inspections prove labor-intensive, inefficient, and prone to errors. This study introduces an innovative approach for river debris detection, employing Unmanned Aerial Vehicles (UAVs) imagery in conjunction with a refined YOLOv5n model. This approach offers three key contributions. Primarily, the YOLOv5n model is bolstered by integrating the Efficient Channel Attention (ECA) module and reshaping the MobileNetV3 backbone to align with MobileNetV3S, thereby significantly streamlining computational demands and model intricacy. Additionally, precision and speed are augmented by eliminating the detection head for larger targets, while decreasing computational requirements. Subsequently, to counter dataset scarcity, we curate a UAV-derived river debris dataset, encompassing five prevalent debris types, serving as an indispensable resource for method refinement and assessment. Lastly, the upgraded model's evaluation on Jetson Nano yields an mAP of 87.2%, merely 0.7% lower than the original YOLOv5n model. Remarkably, the refined model achieves substantial reductions of 57.1% in parameters, 52.6% in volume, and 54.8% in GFLOPs. Additionally, inference time is abbreviated to 57.3ms per Jetson Nano image, 13.4ms faster than the original. These findings underscore edge computing's potential in river restoration. In conclusion, the fusion of deep learning object detection and UAV imagery empowers adept river debris detection. [ABSTRACT FROM AUTHOR]