Automatic fish detection in images plays a crucial role in marine biodiversity monitoring and environmental conservation. The advent of deep learning has significantly improved the results achievable in fish detection; however, underwater challenges such as scale variability, optical distortions, and color inconsistencies reveal some limitations of conventional deep learning-based detectors. To address these issues, we propose an improved fish detection model designed for underwater environments trained to perform joint underwater image enhancement and multi-scale fish detection. While incorporating structural enhancements for multi-scale robustness, such as a dedicated tiny object detection head and attention mechanisms, the core of our approach resides in the joint training of an underwater image enhancement module with the detector. We evaluate our method on two challenging test sets. The experimental results demonstrate that our model significantly outperforms state-of-the-art methods, achieving an average precision between 0.84 and 0.89. Despite its superior accuracy, the proposed solution maintains a lightweight architecture with only 24 millions of parameters and ensures real-time processing capabilities (13 frames per second on edge hardware), highlighting its potential for effective deployment in marine research and autonomous fisheries monitoring. • YOLO-JUICE boosts small-object detection with an extra tiny-object head. • YOLO-JUICE preserves details without extra cost with a space to depth module. • YOLO-JUICE refines spatial and channel features with a spatial-channel attention. • YOLO-JUICE is jointly trained for image classification and enhancement.
Carletti et al. (Fri,) studied this question.