Riverbed litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based...

Underwater litter is widely spread across aquatic environments such as lakes, rivers, and oceans, significantly impacting natural ecosystems. Current automated monitoring technologies for detecting this litter face limitations in survey efficiency, cost, and environmental conditions, highlighting the need for efficient, consumer-grade technologies for automatic detection. This research introduces the Aerial-Aquatic Speedy Scanner (AASS) combined with Super-Resolution Reconstruction (SRR) and an enhanced YOLOv8 detection network. The AASS system boosts data acquisition efficiency over traditional methods, capturing high-resolution images that accurately identify and categorize underwater waste. The SRR technique enhances image quality by mitigating common issues like motion blur and low resolution, thereby improving the YOLOv8 model's detection capabilities. Specifically, the RCAN model achieved the highest mean average precision (mAP) of 78.6 % for object detection accuracy on reconstructed underwater litter among the tested SR models. With a magnification factor of 4, the SR test set shows an improved mAP compared to the Bicubic test set. These results demonstrate the effectiveness of the proposed method in detecting underwater litter.