Review of Deep Learning Techniques for Underwater Image Enhancement, Object Detection, and Few-Shot Object Detection

Abstract

The advancement of underwater object detection plays a primary part in marine archaeology and oceanic surveillance. Nevertheless, underwater images struggle with colour casts and poor illumination, which reduces the accuracy of object detection in underwater scenes. To enhance image quality, various image enhancement techniques are introduced and lead to better detection accuracy. The research explores different underwater image enhancement, few-shot mechanisms, and underwater object detection techniques. Furthermore, the review classifies the deep learning-based techniques in terms of their learning strategies, utilized features, and underlying frameworks. The strengths and limitations of the traditional techniques are analysed to provide deep future insights. Moreover, underwater image enhancement and object detection used diverse datasets and metrics, which are also analysed in the research. Thus, the key findings formulated in the review provide an analytic comparison and future directions.