# Light-YOLO: A Study of a Lightweight YOLOv8n-Based Method for Underwater Fishing Net Detection
## 1 Introduction  
As the marine economy expands and marine technology capabilities mature, the deep-sea fish farming industry has experienced rapid growth [1]. However, this growth brings challenges, notably the susceptibility of seabed nets to corrosion from environmental factors and damage from fish nibbling. Traditional manual diving methods for net inspection could be more efficient and safe in complex underwater environments. The advent of underwater robotics represents a promising solution to these challenges [2], offering safer and potentially more efficient inspection methods. Nevertheless, the dynamic and unpredictable underwater environment complicates the detection of damaged areas in fishing nets [3, 4], which is further exacerbated by the limited computational and storage capacities of underwater robots. In target detection technology, algorithms are divided into two main categories: two-stage and one-stage algorithms. Two-stage algorithms, like R-CNN, Fast R-CNN, and Faster R-CNN [5, 6, 7], streamline the processing by generating candidate target regions in sequences, thereby reducing the number of regions that require detailed analysis. For instance, Faster R-CNN employs a region proposal network (RPN) to create proposals, followed by classification and bounding box regression through a deep CNN, enhancing accuracy in complex scenes. Despite these advancements, the processing speed remains a challenge, particularly for applications requiring rapid or real-time analysis. This necessitates further research into optimizing these algorithms to serve fast-paced underwater operations better. Unlike traditional two-stage target detection algorithms, single-stage target detection algorithms such as the SSD [8] and YOLO [9] series map image pixels directly to bounding box coordinates and category probabilities by simplifying the detection process as a regression problem. In particular, the YOLO family is widely recognized in several research and application areas for its excellent performance and fast processing speed. It is especially suitable for real-time application scenarios such as video surveillance [10] and autonomous driving [11]. However, although the YOLO algorithm performs well in real-time target detection, its performance is limited in specific scenarios, such as underwater environments. Factors such as light refraction, water current interference, and suspended particles in underwater environments often result in low contrast and blurred images, posing challenges for vision-based target detection algorithms. Fishing nets, which have complex textures and are easily integrated with the underwater environment, are particularly difficult to recognize and localize accurately using the YOLO algorithm. Due to the fine structure and large size of fishing nets, it is difficult for YOLO algorithms to capture their overall morphology and detailed features in a single-scale field of view. This may result in small targets (e.g., a part of a fishing net) being ignored and computational resources being wasted or key features of large-size fishing nets being lost, affecting localization and recognition accuracy. In addition, overlapping targets (e.g., multi-layered or partially stacked fishing nets) in an underwater scene increase the difficulty of recognition, and it is challenging to distinguish closely connected or partially occluded objects due to the limited ability of the YOLO algorithm to handle overlapping targets. To optimize this problem, this paper constructs a structure-optimized Light-YOLO model from the YOLOv8 series. The main contributions include the following:  
1. A dataset labelled with underwater fishing net vulnerabilities is created, and the dataset is expanded using GAN network generation techniques. 2. A fast, efficient feature processing module, DA2D, based on sparse linkage and variability convolution is proposed.