# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
## 1 Introduction  
More than 70% of the Earth is covered by water, and our oceans play a vital role in the survival of humans all around the globe. Compared to the level of development on land, the oceans are still veiled in a layer of mystery, holding a vast amount of untapped resources. The marine environment has also been continuously under threat in recent years, making research on the marine environment meaningful. An increasing number of researchers are focusing on the development of underwater technologies, such as underwater acoustics, underwater magnetism [1], underwater vehicle systems, underwater sensing components, and underwater unexplored ordance detection [2], among others. With the advancement of computer vision, exploring the oceans using computer vision technology has becomea new avenue. Underwater optical images have a relatively high resolution and contain rich information, making them particularly advantageous for short-range underwater target detection tasks. Fueled by the winds of computer vision, object detection has gradually become one of the hottest technologies in ocean exploration, making a significant contribution to the development of marine resources. The field of Generic Object Detection [3; 4; 5] has evolved for more than two decades, progressing from traditional methods to the deep learning techniques used at present. Over this time, there has been a notable increase in accuracy, coupled with an improved processing speed. However, Generic Object Detection in the underwater domain still faces significant challenges that need to be addressed. Firstly, underwater environments suffer from issues such as uneven lighting, low contrast, blurriness, and bright spots, which impact the quality of features extracted from underwater images. Secondly, underwater organisms are typically small and densely distributed, often leading to overlapping and occlusion. Detecting small objects is a challenging aspect of generic object detection. Thirdly, underwater embedded devices often have limited computational and storage capabilities, making it difficult to deploy large models on these devices. Therefore, finding solutions to allow for the accurate and rapid detection of objects in complex underwater environments is an urgent problem that needs to be addressed. Existing underwater object detection methods have seen numerous improvements. Most underwater objects are small and densely packed, making it challenging for general detectors to detect these small and blurry objects. [PERSON] et al. [6] introduced a two-stage underwater detector with three key components, addressing uncertainty modeling and hard example mining. The RetinaRPN network, the first component, utilizes objectness and IoU prediction to generate high-quality proposals. The second component, a Probabilistic Inference Pipeline, combines prior uncertainty from the first stage with the second stage's classification score to obtain the final detection score. The third component employs \"Boosting Reweighting\", a novel hard example mining method that amplifies the classification loss of challenging examples while reducing the weight of easy examples. This approach facilitates the acquisition of a robust detection head in the second stage. During the inference stage, the integration of R-CNN rectifies errors from the first stage, resulting in an overall performance improvement. Their work yielded positive results in the domain of underwater images, but the composed backbones were characterized by a large number of parameters, making them unsuitable for real-time applications. [PERSON] et al. [7] proposed a lightweight underwater detector based on YOLOv4 [8], integrating MobileNetv2 [9] and depth-wise separable convolution [10] to reduce the network parameters. This work achieved real-time underwater target detection and performed well compared to traditional detectors. However, there is still room for performance improvements compared to state-of-the-art detectors.