In power operation scenarios, the operators are required to wear gloves of different types depending on the specific task scenario. Due to variations in glove characteristics caused by the uncertainty of the working environment, existing algorithms struggle to effectively identify the type of gloves being worn by workers. To address the challenge of robust feature extraction in complex environments, a fine-grained glove detection algorithm was proposed based on adaptive feature extraction network(AFEN). First, the AFEN algorithm incorporated an adaptive feature extraction (AFE) structure in the feature extraction module, allowing for dynamic adjustments to the size of the feature extraction module to better capture the shape features of varying targets. Second, a global efficient attention mechanism was integrated into the feature extraction module to help the model capture features at different scales and focus more on key features, thereby improving recognition accuracy. Third, a small-object detection structure was incorporated into the detection module to enhance the model's sensitivity to small objects and improved detection performance for such targets. Finally, the AFEN algorithm utilized an adaptive loss function, which enabled the model to more accurately locate targets, further enhancing localization precision. Compared to the state-of-the-art algorithms, the AFEN algorithm achieves a glove type recognition accuracy of 75.7%, a recall rate of 73.2%, an improvement of 2.4%, and an average precision of 76.3%, an improvement of 2.5%, demonstrating its effectiveness and accuracy in fine-grained glove detection, and the glove dataset is released at https://github. com/wtu1020/ElectricGlove-Dataset.