A bird target detection model designed for substation scenarios

Substations are critical to the power grid, but they are often disturbed by bird activity, which can lead to power failures and outages. Conventional bird-detection methods are costly and lack long-term effectiveness. To address these issues, this paper proposes a bird target detection method, YOLO-birds, designed explicitly for substation scenarios. It utilizes the Faster-BiFPN module to optimize feature extraction and fusion by combining low-level and high-level features, thereby enhancing detection accuracy. In addition, the SPPBiF attention mechanism is introduced to address the challenge of detecting targets at different scales and small objects, such as birds. To further improve robustness, the Focal-EIoU loss function is also utilized to mitigate the effect of low-quality samples. To support this research and improve the detection performance in real-world scenarios, a self-constructed dataset of bird images focusing on security threats at substations was created. Experimental results show that YOLO-birds achieves a mAP50 of 90.2%, which validates the effectiveness of the proposed method compared to other methods. The method can efficiently detect birds inhabiting substations, which can help to differentiate the prevention of bird-caused accidents in power grids.