In recent years, the automated inspection approaches have been widely used in power system, which is safer and more efficient than traditional manual inspection. In order to effectively process the large number of power inspection images generated by automated inspection machines, such as unmanned aerial vehicles (UAVs), the deep learning-based object detection technology is commonly used to timely detect potential faults. However, in practical applications, power lines are widely distributed, and the collected inspection images are not easily integrated. In addition, due to data privacy and restrictions from relevant laws and regulations, these inspection images cannot be shared with or provided to third-party organizations. The data exist as “data islands” scattered among various power companies. In this situation, a single power company has a limited amount and variety of inspection image data, making it difficult to obtain a robust object detection model with good generalization performance through traditional data-driven deep learning algorithms. Federated learning (FL), as an emerging distributed technology, can be efficiently applied to visual tasks of power inspection by constructing a high-performance model through collaboration among participants while protecting data privacy. In traditional federated learning, the final distributed model to each participant is a global model with the same performance without considering the contribution differences by different participants, which means different participants receive the same model. However, in real scenarios, the quantity, quality, and cost of power inspection image data provided by different participants are really different, and their contributions to the training of global model are also different. If the participants in federal learning cannot receive fair and reasonable returns, it will cause some participants to be unwilling to participate in the federal training of object detection model for processing power inspection images, and cannot guarantee the number of participants in federal learning, resulting in the inability to continue federal training or poor training performance. To address these challenges and incentivize more participants to join federated learning for object detection of power inspection images, this paper proposes two federated incentive mechanisms based on model fairness and revenue fairness, respectively. The model fairness incentive mechanism is designed for the scenarios where all participants are data owners, and it involves evaluating contributions to obtain models with varying performance levels. In this incentive mechanism, we use an evaluation strategy based on marginal benefit, and compare the contribution measurement methods based on unilateral training, dynamic accuracy, and impact function. The revenue fairness incentive mechanism targets the scenarios involving both data owners and data demanders, wherein data owners receive corresponding benefits, and data demanders obtain high-performance models. In this incentive mechanism, the fairness of the federated learning is modeled by evaluating contributions, expected losses, and expected temporal losses. The above two incentive mechanisms are validated by the experiments on the homemade UAV power tower inspection image dataset. The three kinds of data heterogeneity are considered including feature skew, quantity skew and mixed skew. The results demonstrate that the fairness correlation coefficients in the two incentive mechanisms can reach 0.96 and 1, respectively. This indicates that the proposed incentive mechanisms can effectively enhance fairness and motivate more participants to engage in federated learning for object detection of power inspection images. Also, it is noted that although we use power object detection as the main application scenario in this work, the proposed incentive mechanism can be applied to various power vision tasks including image classification, object detection, and anomaly detection, and has a certain degree of generality.