  Marine debris poses a significant ecological threat to birds, fish, and other
animal life. Traditional methods for assessing debris accumulation involve
labor-intensive and costly manual surveys. This study introduces a framework
that utilizes aerial imagery captured by drones to conduct remote trash
surveys. Leveraging computer vision techniques, our approach detects,
classifies, and maps marine debris distributions. The framework uses Grounding
DINO, a transformer-based zero-shot object detector, and CLIP, a
vision-language model for zero-shot object classification, enabling the
detection and classification of debris objects based on material type without
the need for training labels. To mitigate over-counting due to different views
of the same object, Scale-Invariant Feature Transform (SIFT) is employed for
duplicate matching using local object features. Additionally, we have developed
a user-friendly web application that facilitates end-to-end analysis of drone
images, including object detection, classification, and visualization on a map
to support cleanup efforts. Our method achieves competitive performance in
detection (0.69 mean IoU) and classification (0.74 F1 score) across seven
debris object classes without labeled data, comparable to state-of-the-art
supervised methods. This framework has the potential to streamline automated
trash sampling surveys, fostering efficient and sustainable community-led
cleanup initiatives.
