Underwater object detection is particularly challenging due to the inherent distortions, scattering, and light attenuation present in aquatic environments, which degrade visual clarity and impact detection accuracy. This paper outlines a comprehensive approach to enhance object detection in such challenging conditions. The proposed methodology combines morphological edge enhancement techniques with an efficient detection model featuring bottleneck layers. The initial phase involves the acquisition of a meticulously labeled dataset comprising underwater images containing objects of interest. Prior to model training, a critical preprocessing step is undertaken to rectify underwater distortions, encompassing tasks like color correction and contrast enhancement. To further fortify the proposed method adaptability to various conditions in underwater, the dataset is enriched through augmentation, introducing variations in lighting conditions, water clarity, and object poses. Bottleneck layers act as information bottlenecks, reducing the dimensionality of features while simultaneously enhancing their depth. This transformation not only compresses information but also mitigates computational overhead, thereby facilitating efficient object detection. This proposed model undergoes experimental validation on the underwater dataset, achieving significantly higher metrics such as a mean average precision (mAP) of 85.1%, precision of 84.4%, and recall of 79.9%. These experimental findings strongly indicate that the suggested method surpasses current models in its ability to detect exceedingly underwater objects effectively.
Vasanthi et al. (Sun,) studied this question.