# RHS-YOLOv8: A Lightweight Underwater Small Object Detection Algorithm Based on Improved YOLOv8
## 1 Introduction  
Precise detection and localization of underwater resources are essential for the sustainable management of marine ecosystems. Nowadays, unmanned vehicles for underwater detection (UUVs) [1] are indispensable in marine research, seafloor mapping, and various underwater missions to assist in mapping the seafloor, identifying potential obstacles, and recognizing both topographical and biological features, as well as inspecting underwater infrastructure. Currently, underwater object detection has a wide range of applications, from monitoring marine species [2] to underwater archaeology [3], aquaculture [4], and military operations [5]. Underwater object detection faces many challenges. First, underwater images, unlike normal land images, exhibit a significant reduction in contrast between objects and their background. This effect arises from the inherent uncertainty of the underwater environment, light absorption and scattering by water, and the presence of various particulate media (e.g., coral reefs and sediments) [6]. Secondly, factors such as water currents, suspended particles, and bubbles can introduce blurring and distortion into images, further reducing recognition accuracy [7]. In addition, objects in the underwater environment are usually numerous, small in size, and diverse in shape, and the objects easily obscure each other, which puts forward higher requirements on the feature extraction and multi-scale feature fusion ability of the detection model [8]. Finally, various forms of noise and disturbances, including water waves, air bubbles, and floating debris, further disrupt the detection and recognition process [9]. Although existing conventional object detection techniques are effective at extracting shallow features of an object such as shape, contour, and texture [10; 11; 12; 13], the recognition of these features relies on manual design and is poorly adapted to environmental variations (e.g., changes in water quality and changes in illumination). In addition, these methods find it difficult to accurately separate target objects in complex backgrounds, often lack precision and efficiency, and exhibit poor performance in detecting effects in complex underwater environments. With advances in artificial intelligence, deep learning-based object detection and classification techniques have enabled rapid object detection in complex underwater environments. Deep learning-based object detection methods are primarily divided into two categories: two-stage detection models and single-stage detection models. Among them, the most prominent two-stage models are R-CNN [14], Fast-RCNN [15], and Faster-RCNN [16]. The main feature of the two-stage model is that the object detection task is divided into two stages: region proposal and classification regression. Although it has some advantages in detection accuracy, the model structure is larger, with low computational speed and high complexity, which is not applicable to real-time object detection in underwater monitoring scenarios. The other category is single-stage algorithms based on regression, such as the RetinaNet [17], SSD series [18; 19], YOLO series [20; 21; 22; 23], and so on. The single-stage algorithms have good real-time performance and are suitable for scenarios with limited storage and computational performance on underwater detection hardware devices, but have the disadvantage of low accuracy and need to be optimized, especially in the detection of small objects. The primary representative of single-stage models is the YOLO series, characterized by its ability to take images directly as input and produce detection results in a single step. Its main advantages include a simple architecture, compact model size, and high processing speed. Compared with other models, it is more suitable for object detection in underwater images, and how to further lighten the algorithm of object detection and ensure the accuracy of detection is the difficulty of current research.