Joint optimization technique of image enhancement and object detection based on a genetic algorithm for underwater target detection

Underwater images often suffer from severe degradation issues such as color distortion and haze, significantly impacting object detection performance. Most existing approaches treat image enhancement and object detection as two separate tasks, first enhancing the image, then performing object detection. However, the enhancement process does not necessarily improve the detection accuracy due to the lack of effective interaction between these two tasks. To address this limitation, we propose, to our knowledge, a novel joint optimization framework for underwater image enhancement and object detection driven by a genetic algorithm (GA). The proposed framework consists of three key components: an underwater image enhancement module (UIEM), an object detection module (ODM), and a genetic optimization module. The UIEM improves image quality by correcting color distortion and removing haze, while the ODM performs object detection on the enhanced images and evaluates the detection performance using mean average precision (mAP). To optimize the synergy between enhancement and detection, we employ GA as the core optimization strategy, using mAP as the fitness function. Through evolutionary operations such as selection, crossover, and mutation, GA adaptively tunes the enhancement parameters to improve detection accuracy. Extensive experiments conducted on two public underwater datasets, URPC2019 and UTDAC2020, demonstrate that the proposed method significantly increases the detection performance of state-of-the-art detectors, including YOLOv5, Faster R-CNN, and RetinaNet, in challenging underwater environments.