  Underwater robotic vision encounters significant challenges, necessitating
advanced solutions to enhance performance and adaptability. This paper presents
MARS (Multi-Scale Adaptive Robotics Vision), a novel approach to underwater
object detection tailored for diverse underwater scenarios. MARS integrates
Residual Attention YOLOv3 with Domain-Adaptive Multi-Scale Attention (DAMSA) to
enhance detection accuracy and adapt to different domains. During training,
DAMSA introduces domain class-based attention, enabling the model to emphasize
domain-specific features. Our comprehensive evaluation across various
underwater datasets demonstrates MARS's performance. On the original dataset,
MARS achieves a mean Average Precision (mAP) of 58.57\%, showcasing its
proficiency in detecting critical underwater objects like echinus, starfish,
holothurian, scallop, and waterweeds. This capability holds promise for
applications in marine robotics, marine biology research, and environmental
monitoring. Furthermore, MARS excels at mitigating domain shifts. On the
augmented dataset, which incorporates all enhancements (+Domain
+Residual+Channel Attention+Multi-Scale Attention), MARS achieves an mAP of
36.16\%. This result underscores its robustness and adaptability in recognizing
objects and performing well across a range of underwater conditions. The source
code for MARS is publicly available on GitHub at
https://github.com/LyesSaadSaoud/MARS-Object-Detection/
