A Real-Time Framework for Domain-Adaptive Underwater Object Detection
  with Image Enhancement
Junjie Wen Jinqiang Cui Benyun Zhao Bingxin Han Xuchen Liu Zhi Gao Ben M. Chen
In recent years, significant progress has been made in the field of
underwater image enhancement (UIE). However, its practical utility for
high-level vision tasks, such as underwater object detection (UOD) in
Autonomous Underwater Vehicles (AUVs), remains relatively unexplored. It may be
attributed to several factors: (1) Existing methods typically employ UIE as a
pre-processing step, which inevitably introduces considerable computational
overhead and latency. (2) The process of enhancing images prior to training
object detectors may not necessarily yield performance improvements. (3) The
complex underwater environments can induce significant domain shifts across
different scenarios, seriously deteriorating the UOD performance. To address
these challenges, we introduce EnYOLO, an integrated real-time framework
designed for simultaneous UIE and UOD with domain-adaptation capability.
Specifically, both the UIE and UOD task heads share the same network backbone
and utilize a lightweight design. Furthermore, to ensure balanced training for
both tasks, we present a multi-stage training strategy aimed at consistently
enhancing their performance. Additionally, we propose a novel domain-adaptation
strategy to align feature embeddings originating from diverse underwater
environments. Comprehensive experiments demonstrate that our framework not only
achieves state-of-the-art (SOTA) performance in both UIE and UOD tasks, but
also shows superior adaptability when applied to different underwater
scenarios. Our efficiency analysis further highlights the substantial potential
of our framework for onboard deployment.