Due to the complex background and small target of the transmission line cotter pin, the intelligent power inspection of unmanned aerial vehicles (UAVs) is vulnerable to the problems of low detection accuracy and high missed and false detection rates. Addressing these issues, the present study proposes a target detection algorithm based on YOLOv8-DEA to better adapt to UAVs and other application scenarios. First, the backbone network's C2f module is modified, enabling the model to focus on regions of interest and enhancing its perception of local image structures. Subsequently, an efficient mamba-like linear attention (EMLLA) mechanism is used to capture distant dependencies, and the efficient multilayer perceptron (EMLP) module is applied to map the model features to a higher dimensionality, enhancing the model's expressiveness. Finally, a dynamic selection mechanism is used to improve the Neck layer. The adaptive fusion of deep and shallow features enables the effective integration of features from different levels, allowing the model to accurately capture global semantic information, as well as extract rich detailed information when processing complex and diverse data. The experimental results demonstrate that the improved algorithm achieves a 2.33 percentage points increase in mean average precision (mAP@0.5) and 3.67 percentage points increase in recall (R@0.5) on the custom cotter pin dataset. Additionally, the algorithm achieves a precision of 95.58% and speed of 67.84 frame/s. When compared to mainstream algorithms, the proposed method not only exhibits improved detection accuracy, but also ensures real-time performance, making it more suitable for the needs of transmission-line cotter pin detection in engineering applications.