## 1 Introduction
introduced an enhanced YOLOv5 algorithm, replacing the C3 module in the backbone network with the C2f module and optimizing the loss function. This modification allows for the accurate and effective identification and localization of damage in X-ray images of steel cord conveyor belts [23]. [PERSON] et al. utilized the YOLOv3 algorithm to integrate candidate box extraction, feature extraction, target classification, and target location into a neural network. This integrated approach allows for the automatic detection and identification of defects and faults in insulators, bird's nests, dampers, conductors, towers, and other critical components [24]. In 2022, the Ultralytics team published the source code of the YOLOv8 model [25; 26], offering five models with varying scales and channel numbers based on scaling coefficients -_n_, \(s\), \(m\), \(l\), and \(x\). These models are designed to cater to diverse scenarios, ensuring a balance between real-time performance and accuracy. Since 2023, the YOLOv8 has been extensively utilized across various categories of target detection fields [27]. [PERSON] et al. introduced a lightweight enhanced model derived from YOLOv8 [28]. They incorporated a specialized small target detection layer into the feature fusion network and introduced a Scale Sequence Feature Fusion (SSFF) module to enhance multi-scale feature fusion, enabling the model to capture more gradient paths. These enhancements led to improved accuracy while simultaneously reducing model parameters. [PERSON] et al. integrated a novel dual self-focusing mechanism into the backbone of YOLOv8. They implemented InnerShape Intersection over Union (IS-IoU) as the boundary box regression loss and refined the feature fusion component. This approach significantly enhanced the detection capability of small targets [29]. The existing YOLO algorithm encounters several challenges in practical applications, primarily including inadequate positional accuracy, limited feature representation capability, and difficulty in handling long-tail distributed data. Due to the constraints of its framework, the YOLO algorithm exhibits low positional accuracy when dealing with small and dense targets, resulting in suboptimal detection performance in complex scenarios. Moreover, YOLO's feature extraction network often struggles to extract crucial information from images in the presence of intricate backgrounds and multi-scale targets, consequently impacting overall detection effectiveness. In real-world scenarios, target categories frequently exhibit long-tail distribution characteristics, wherein a few categories have abundant samples while most categories have limited samples. This characteristic makes it easy for the YOLO to overlook categories with fewer examples when processing long-tail distribution data. To address these challenges, some researchers have enhanced the YOLO algorithm by refining the loss function and introducing a new feature extraction module to enhance its detection accuracy and robustness. To solve the above problems, this paper proposes an improved SL-YOLOv8 algorithm based on YOLOv8, which adds SENet_v2 and LSK modules to the feature detection head of the YOLOv8 model. SENet_v2 augments the YOLO algorithm's feature representation capabilities by incorporating an attention mechanism. Specifically, SENet_v2 enhances detection performance for small targets and intricate backgrounds by adaptively recalibrating channel relationships, directing the network to prioritize important features. The LSK module elevates YOLO's ability to detect objects of varying scales through the utilization of a large convolutional kernel in feature extraction. Large convolutional kernels can capture a broader context range, significantly enhancing target detection accuracy, especially in scenarios involving multi-scale targets. With these enhancements, SL-YOLOv8 outperforms the original YOLO model across multiple benchmark datasets, particularly excelling in small target detection and complex scenes, showing higher precision and robustness.