  Underwater litter is widely spread across aquatic environments such as lakes,
rivers, and oceans, significantly impacting natural ecosystems. Current
monitoring technologies for detecting underwater 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 improved YOLOv8 detection network.
AASS enhances data acquisition efficiency over traditional methods, capturing
high-quality images that accurately identify underwater waste. SRR improves
image-resolution by mitigating motion blur and insufficient resolution, thereby
enhancing detection tasks. Specifically, the RCAN model achieved the highest
mean average precision (mAP) of 78.6% for detection accuracy on reconstructed
images among the tested SRR models. With a magnification factor of 4, the SRR
test set shows an improved mAP compared to the conventional bicubic set. These
results demonstrate the effectiveness of the proposed method in detecting
underwater litter.
