  Marine debris is an important issue for environmental protection, but current
methods for locating marine debris are yet limited. In order to achieve higher
efficiency and wider applicability in the localization of Marine debris, this
study tries to combine the instance segmentation of YOLOv7 with different
attention mechanisms and explores the best model. By utilizing a labelled
dataset consisting of satellite images containing ocean debris, we examined
three attentional models including lightweight coordinate attention, CBAM
(combining spatial and channel focus), and bottleneck transformer (based on
self-attention). Box detection assessment revealed that CBAM achieved the best
outcome (F1 score of 77%) compared to coordinate attention (F1 score of 71%)
and YOLOv7/bottleneck transformer (both F1 scores around 66%). Mask evaluation
showed CBAM again leading with an F1 score of 73%, whereas coordinate attention
and YOLOv7 had comparable performances (around F1 score of 68%/69%) and
bottleneck transformer lagged behind at F1 score of 56%. These findings suggest
that CBAM offers optimal suitability for detecting marine debris. However, it
should be noted that the bottleneck transformer detected some areas missed by
manual annotation and displayed better mask precision for larger debris pieces,
signifying potentially superior practical performance.
