# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
## 2 Related Work
### Small-Object Detection  
Recent advancements in generic object detection have been achieved through the application of deep learning techniques. However, detecting small objects in images remains a complex challenge due to their limited size, subtle appearance, and intricate geometry cues. Compounding this difficulty is the absence of extensive datasets dedicated to small objects. Improving the ability to detect small objects holds great practical significance in real-world applications, such as underwater robotics, autonomous driving for vehicles, and drone-based detection, among others.  
Current trends in small-object detection encompass key techniques such as multiscale representation, contextual information, super-resolution, and region proposal. Multiscale representation combines specific location details extracted from low-level feature maps with abundant semantic information derived from high-level feature maps. For instance, the Feature Pyramid Network (FPN) [32] algorithm simultaneously utilizes low-level features with a high resolution and high-level features with high levels of semantic information. By fusing features from different layers, it achieves effective predictions. Experiments suggest that simply increasing the depth of the network may not be an effective solution to the challenge of detecting small objects. Small-object detection benefits from the fusion of high-resolution and semantically rich feature maps. Leveraging the relationship between an object and its surrounding environment is a novel approach to improving small-object detection accuracy. Extracting additional contextual information as a supplement to the original region of interest (ROI) features is crucial since the ROI features extracted from small objects are often limited. Attention mechanisms are one example of a technique inspired by cognitive attention in artificial neural networks. These mechanisms enhance the importance of certain parts of the input data while reducing the importance of others based on context. They are trained using gradient descent. Super-resolution techniques aim to enhance or reconstruct low-resolution images to a higher resolution, allowing for the recovery of more details, especially for small objects. For instance, SRGAN [33] was the first paper to apply GANs to the super-resolution domain. It combined GANs with SRResNet [34], introducing new loss functions such as content loss and adversarial loss to address the challenge of recovering high-frequency information in super-resolution. Region proposal is a strategy to design more suitable anchors for small objects. For example, YOLOv2 [35] uses anchor boxes to predict bounding boxes, effectively improving the model recall capability, which is particularly beneficial for small-object detection.  
Currently, deep-learning-based small-object detection has found numerous applications, such as garbage waste management in smart cities [36]. In our work, we enhanced small-object detection by incorporating three key techniques: a prediction head for small objects utilizing multiscale representation, coordinate attention [13] to improve semantic information in feature maps without a significant increase in computational load, and Deformable ConvNets v2 [12] convolution for adaptively learning feature point receptive fields, ultimately enhancing detection accuracy, particularly for small objects in complex environments.